id	sid	tid	token	lemma	pos
ajst-25954	1	1	academic	academic	ADJ
ajst-25954	1	2	journal	journal	NOUN
ajst-25954	1	3	of	of	ADP
ajst-25954	1	4	science	science	NOUN
ajst-25954	1	5	and	and	CCONJ
ajst-25954	1	6	technology	technology	NOUN
ajst-25954	1	7	issn	issn	NOUN
ajst-25954	1	8	:	:	PUNCT
ajst-25954	1	9	2771	2771	NUM
ajst-25954	1	10	-	-	SYM
ajst-25954	1	11	3032	3032	NUM
ajst-25954	1	12	|	|	NOUN
ajst-25954	1	13	vol	vol	NOUN
ajst-25954	1	14	.	.	PROPN
ajst-25954	2	1	12	12	NUM
ajst-25954	2	2	,	,	PUNCT
ajst-25954	2	3	no	no	INTJ
ajst-25954	2	4	.	.	NOUN
ajst-25954	2	5	3	3	NUM
ajst-25954	2	6	,	,	PUNCT
ajst-25954	2	7	2024	2024	NUM
ajst-25954	2	8	81	81	NUM
ajst-25954	2	9	parallel	parallel	ADJ
ajst-25954	2	10	channel	channel	NOUN
ajst-25954	2	11	feature	feature	NOUN
ajst-25954	2	12	weighted	weight	VERB
ajst-25954	2	13	seizure	seizure	NOUN
ajst-25954	2	14	prediction	prediction	NOUN
ajst-25954	2	15	based	base	VERB
ajst-25954	2	16	on	on	ADP
ajst-25954	2	17	multi‐scale	multi‐scale	PROPN
ajst-25954	2	18	spatial	spatial	ADJ
ajst-25954	2	19	and	and	CCONJ
ajst-25954	2	20	temporal	temporal	ADJ
ajst-25954	2	21	factorization	factorization	NOUN
ajst-25954	2	22	jinying	jinye	VERB
ajst-25954	2	23	han1	han1	NOUN
ajst-25954	2	24	,	,	PUNCT
ajst-25954	2	25	*	*	PUNCT
ajst-25954	2	26	1the	1the	PRON
ajst-25954	2	27	school	school	NOUN
ajst-25954	2	28	of	of	ADP
ajst-25954	2	29	computer	computer	NOUN
ajst-25954	2	30	science	science	NOUN
ajst-25954	2	31	and	and	CCONJ
ajst-25954	2	32	technology	technology	NOUN
ajst-25954	2	33	,	,	PUNCT
ajst-25954	2	34	henan	henan	PROPN
ajst-25954	2	35	polytechnic	polytechnic	PROPN
ajst-25954	2	36	university	university	PROPN
ajst-25954	2	37	,	,	PUNCT
ajst-25954	2	38	jiaozuo	jiaozuo	PROPN
ajst-25954	2	39	454003	454003	NUM
ajst-25954	2	40	,	,	PUNCT
ajst-25954	2	41	china	china	PROPN
ajst-25954	2	42	*	*	PUNCT
ajst-25954	2	43	corresponding	correspond	VERB
ajst-25954	2	44	author	author	NOUN
ajst-25954	2	45	abstract	abstract	NOUN
ajst-25954	2	46	:	:	PUNCT
ajst-25954	2	47	epileptic	epileptic	ADJ
ajst-25954	2	48	seizure	seizure	NOUN
ajst-25954	2	49	prediction	prediction	NOUN
ajst-25954	2	50	based	base	VERB
ajst-25954	2	51	on	on	ADP
ajst-25954	2	52	electroencephalography	electroencephalography	NOUN
ajst-25954	2	53	(	(	PUNCT
ajst-25954	2	54	eeg	eeg	NOUN
ajst-25954	2	55	)	)	PUNCT
ajst-25954	2	56	plays	play	VERB
ajst-25954	2	57	an	an	DET
ajst-25954	2	58	important	important	ADJ
ajst-25954	2	59	role	role	NOUN
ajst-25954	2	60	in	in	ADP
ajst-25954	2	61	the	the	DET
ajst-25954	2	62	field	field	NOUN
ajst-25954	2	63	.	.	PUNCT
ajst-25954	3	1	however	however	ADV
ajst-25954	3	2	,	,	PUNCT
ajst-25954	3	3	the	the	DET
ajst-25954	3	4	existing	exist	VERB
ajst-25954	3	5	epilepsy	epilepsy	NOUN
ajst-25954	3	6	prediction	prediction	NOUN
ajst-25954	3	7	methods	method	NOUN
ajst-25954	3	8	have	have	VERB
ajst-25954	3	9	little	little	ADJ
ajst-25954	3	10	modeling	modeling	NOUN
ajst-25954	3	11	ability	ability	NOUN
ajst-25954	3	12	to	to	PART
ajst-25954	3	13	capture	capture	VERB
ajst-25954	3	14	the	the	DET
ajst-25954	3	15	interaction	interaction	NOUN
ajst-25954	3	16	between	between	ADP
ajst-25954	3	17	features	feature	NOUN
ajst-25954	3	18	,	,	PUNCT
ajst-25954	3	19	and	and	CCONJ
ajst-25954	3	20	the	the	DET
ajst-25954	3	21	high	high	ADJ
ajst-25954	3	22	redundancy	redundancy	NOUN
ajst-25954	3	23	of	of	ADP
ajst-25954	3	24	features	feature	NOUN
ajst-25954	3	25	leads	lead	VERB
ajst-25954	3	26	to	to	ADP
ajst-25954	3	27	the	the	DET
ajst-25954	3	28	limitations	limitation	NOUN
ajst-25954	3	29	of	of	ADP
ajst-25954	3	30	model	model	NOUN
ajst-25954	3	31	performance	performance	NOUN
ajst-25954	3	32	.	.	PUNCT
ajst-25954	4	1	in	in	ADP
ajst-25954	4	2	addition	addition	NOUN
ajst-25954	4	3	,	,	PUNCT
ajst-25954	4	4	the	the	DET
ajst-25954	4	5	feature	feature	NOUN
ajst-25954	4	6	information	information	NOUN
ajst-25954	4	7	guided	guide	VERB
ajst-25954	4	8	by	by	ADP
ajst-25954	4	9	the	the	DET
ajst-25954	4	10	multichannel	multichannel	PROPN
ajst-25954	4	11	spatial	spatial	ADJ
ajst-25954	4	12	location	location	NOUN
ajst-25954	4	13	of	of	ADP
ajst-25954	4	14	the	the	DET
ajst-25954	4	15	brain	brain	NOUN
ajst-25954	4	16	region	region	NOUN
ajst-25954	4	17	is	be	AUX
ajst-25954	4	18	ignored	ignore	VERB
ajst-25954	4	19	.	.	PUNCT
ajst-25954	5	1	to	to	PART
ajst-25954	5	2	solve	solve	VERB
ajst-25954	5	3	these	these	DET
ajst-25954	5	4	problems	problem	NOUN
ajst-25954	5	5	,	,	PUNCT
ajst-25954	5	6	this	this	DET
ajst-25954	5	7	paper	paper	NOUN
ajst-25954	5	8	proposes	propose	VERB
ajst-25954	5	9	a	a	DET
ajst-25954	5	10	parallel	parallel	ADJ
ajst-25954	5	11	channel	channel	NOUN
ajst-25954	5	12	featureweighted	featureweighte	VERB
ajst-25954	5	13	seizure	seizure	NOUN
ajst-25954	5	14	prediction	prediction	NOUN
ajst-25954	5	15	network	network	NOUN
ajst-25954	5	16	based	base	VERB
ajst-25954	5	17	on	on	ADP
ajst-25954	5	18	multi	multi	ADJ
ajst-25954	5	19	-	-	ADJ
ajst-25954	5	20	scale	scale	ADJ
ajst-25954	5	21	temporal	temporal	ADJ
ajst-25954	5	22	and	and	CCONJ
ajst-25954	5	23	spatial	spatial	ADJ
ajst-25954	5	24	factorization	factorization	NOUN
ajst-25954	5	25	(	(	PUNCT
ajst-25954	5	26	ms	ms	PROPN
ajst-25954	5	27	-	-	PUNCT
ajst-25954	5	28	stfm	stfm	NOUN
ajst-25954	5	29	-	-	PUNCT
ajst-25954	5	30	pcfwnet	pcfwnet	NOUN
ajst-25954	5	31	)	)	PUNCT
ajst-25954	5	32	.	.	PUNCT
ajst-25954	6	1	specifically	specifically	ADV
ajst-25954	6	2	,	,	PUNCT
ajst-25954	6	3	the	the	DET
ajst-25954	6	4	feature	feature	NOUN
ajst-25954	6	5	information	information	NOUN
ajst-25954	6	6	of	of	ADP
ajst-25954	6	7	time	time	NOUN
ajst-25954	6	8	domain	domain	NOUN
ajst-25954	6	9	and	and	CCONJ
ajst-25954	6	10	multi	multi	ADJ
ajst-25954	6	11	-	-	ADJ
ajst-25954	6	12	channel	channel	ADJ
ajst-25954	6	13	spatial	spatial	ADJ
ajst-25954	6	14	domain	domain	NOUN
ajst-25954	6	15	of	of	ADP
ajst-25954	6	16	brain	brain	NOUN
ajst-25954	6	17	region	region	NOUN
ajst-25954	6	18	can	can	AUX
ajst-25954	6	19	be	be	AUX
ajst-25954	6	20	extracted	extract	VERB
ajst-25954	6	21	by	by	ADP
ajst-25954	6	22	using	use	VERB
ajst-25954	6	23	feature	feature	NOUN
ajst-25954	6	24	matrix	matrix	NOUN
ajst-25954	6	25	to	to	PART
ajst-25954	6	26	fully	fully	ADV
ajst-25954	6	27	learn	learn	VERB
ajst-25954	6	28	the	the	DET
ajst-25954	6	29	correlation	correlation	NOUN
ajst-25954	6	30	between	between	ADP
ajst-25954	6	31	channels	channel	NOUN
ajst-25954	6	32	.	.	PUNCT
ajst-25954	7	1	secondly	secondly	ADV
ajst-25954	7	2	,	,	PUNCT
ajst-25954	7	3	the	the	DET
ajst-25954	7	4	multi	multi	ADJ
ajst-25954	7	5	-	-	ADJ
ajst-25954	7	6	scale	scale	ADJ
ajst-25954	7	7	spatiotemporal	spatiotemporal	ADJ
ajst-25954	7	8	factorizer	factorizer	NOUN
ajst-25954	7	9	(	(	PUNCT
ajst-25954	7	10	ms	ms	PROPN
ajst-25954	7	11	-	-	PUNCT
ajst-25954	7	12	stfm	stfm	NOUN
ajst-25954	7	13	)	)	PUNCT
ajst-25954	7	14	is	be	AUX
ajst-25954	7	15	utilized	utilize	VERB
ajst-25954	7	16	to	to	PART
ajst-25954	7	17	combine	combine	VERB
ajst-25954	7	18	and	and	CCONJ
ajst-25954	7	19	interact	interact	VERB
ajst-25954	7	20	the	the	DET
ajst-25954	7	21	features	feature	NOUN
ajst-25954	7	22	,	,	PUNCT
ajst-25954	7	23	and	and	CCONJ
ajst-25954	7	24	the	the	DET
ajst-25954	7	25	correlation	correlation	NOUN
ajst-25954	7	26	information	information	NOUN
ajst-25954	7	27	between	between	ADP
ajst-25954	7	28	the	the	DET
ajst-25954	7	29	features	feature	NOUN
ajst-25954	7	30	is	be	AUX
ajst-25954	7	31	captured	capture	VERB
ajst-25954	7	32	.	.	PUNCT
ajst-25954	8	1	finally	finally	ADV
ajst-25954	8	2	,	,	PUNCT
ajst-25954	8	3	by	by	ADP
ajst-25954	8	4	combining	combine	VERB
ajst-25954	8	5	the	the	DET
ajst-25954	8	6	multi	multi	ADJ
ajst-25954	8	7	-	-	ADJ
ajst-25954	8	8	scale	scale	ADJ
ajst-25954	8	9	inception	inception	NOUN
ajst-25954	8	10	module	module	NOUN
ajst-25954	8	11	with	with	ADP
ajst-25954	8	12	an	an	DET
ajst-25954	8	13	efficient	efficient	ADJ
ajst-25954	8	14	channel	channel	NOUN
ajst-25954	8	15	attention	attention	NOUN
ajst-25954	8	16	mechanism	mechanism	NOUN
ajst-25954	8	17	,	,	PUNCT
ajst-25954	8	18	a	a	DET
ajst-25954	8	19	parallel	parallel	ADJ
ajst-25954	8	20	channel	channel	NOUN
ajst-25954	8	21	feature	feature	NOUN
ajst-25954	8	22	weighted	weight	VERB
ajst-25954	8	23	network	network	NOUN
ajst-25954	8	24	(	(	PUNCT
ajst-25954	8	25	pcfwnet	pcfwnet	NOUN
ajst-25954	8	26	)	)	PUNCT
ajst-25954	8	27	is	be	AUX
ajst-25954	8	28	constructed	construct	VERB
ajst-25954	8	29	to	to	PART
ajst-25954	8	30	effectively	effectively	ADV
ajst-25954	8	31	learn	learn	VERB
ajst-25954	8	32	multi	multi	ADJ
ajst-25954	8	33	-	-	ADJ
ajst-25954	8	34	domain	domain	ADJ
ajst-25954	8	35	features	feature	NOUN
ajst-25954	8	36	and	and	CCONJ
ajst-25954	8	37	map	map	VERB
ajst-25954	8	38	the	the	DET
ajst-25954	8	39	discriminant	discriminant	ADJ
ajst-25954	8	40	representation	representation	NOUN
ajst-25954	8	41	of	of	ADP
ajst-25954	8	42	epilepsy	epilepsy	NOUN
ajst-25954	8	43	prediction	prediction	NOUN
ajst-25954	8	44	.	.	PUNCT
ajst-25954	9	1	the	the	DET
ajst-25954	9	2	proposed	propose	VERB
ajst-25954	9	3	ms	ms	PROPN
ajst-25954	9	4	-	-	PUNCT
ajst-25954	9	5	stfm	stfm	NOUN
ajst-25954	9	6	-	-	PUNCT
ajst-25954	9	7	pcfwnet	pcfwnet	NOUN
ajst-25954	9	8	is	be	AUX
ajst-25954	9	9	evaluated	evaluate	VERB
ajst-25954	9	10	on	on	ADP
ajst-25954	9	11	public	public	ADJ
ajst-25954	9	12	chb	chb	NOUN
ajst-25954	9	13	-	-	PUNCT
ajst-25954	9	14	mit	mit	NOUN
ajst-25954	9	15	and	and	CCONJ
ajst-25954	9	16	bonn	bonn	PROPN
ajst-25954	9	17	datasets	dataset	NOUN
ajst-25954	9	18	.	.	PUNCT
ajst-25954	10	1	the	the	DET
ajst-25954	10	2	experimental	experimental	ADJ
ajst-25954	10	3	results	result	NOUN
ajst-25954	10	4	show	show	VERB
ajst-25954	10	5	that	that	SCONJ
ajst-25954	10	6	compared	compare	VERB
ajst-25954	10	7	with	with	ADP
ajst-25954	10	8	the	the	DET
ajst-25954	10	9	most	most	ADV
ajst-25954	10	10	advanced	advanced	ADJ
ajst-25954	10	11	methods	method	NOUN
ajst-25954	10	12	,	,	PUNCT
ajst-25954	10	13	the	the	DET
ajst-25954	10	14	proposed	propose	VERB
ajst-25954	10	15	method	method	NOUN
ajst-25954	10	16	achieves	achieve	VERB
ajst-25954	10	17	excellent	excellent	ADJ
ajst-25954	10	18	predictive	predictive	ADJ
ajst-25954	10	19	performance	performance	NOUN
ajst-25954	10	20	,	,	PUNCT
ajst-25954	10	21	which	which	PRON
ajst-25954	10	22	can	can	AUX
ajst-25954	10	23	be	be	AUX
ajst-25954	10	24	used	use	VERB
ajst-25954	10	25	for	for	ADP
ajst-25954	10	26	early	early	ADJ
ajst-25954	10	27	warning	warning	NOUN
ajst-25954	10	28	of	of	ADP
ajst-25954	10	29	epileptic	epileptic	ADJ
ajst-25954	10	30	seizures	seizure	NOUN
ajst-25954	10	31	in	in	ADP
ajst-25954	10	32	specific	specific	ADJ
ajst-25954	10	33	patients	patient	NOUN
ajst-25954	10	34	.	.	PUNCT
ajst-25954	11	1	keywords	keyword	NOUN
ajst-25954	11	2	:	:	PUNCT
ajst-25954	11	3	eeg	eeg	NOUN
ajst-25954	11	4	,	,	PUNCT
ajst-25954	11	5	seizure	seizure	NOUN
ajst-25954	11	6	prediction	prediction	NOUN
ajst-25954	11	7	,	,	PUNCT
ajst-25954	11	8	multiscale	multiscale	NOUN
ajst-25954	11	9	features	feature	NOUN
ajst-25954	11	10	,	,	PUNCT
ajst-25954	11	11	spatio	spatio	NOUN
ajst-25954	11	12	-	-	PUNCT
ajst-25954	11	13	temporal	temporal	ADJ
ajst-25954	11	14	factorization	factorization	NOUN
ajst-25954	11	15	,	,	PUNCT
ajst-25954	11	16	attention	attention	NOUN
ajst-25954	11	17	.	.	PUNCT
ajst-25954	12	1	1	1	X
ajst-25954	12	2	.	.	X
ajst-25954	12	3	introduction	introduction	NOUN
ajst-25954	12	4	as	as	ADP
ajst-25954	12	5	a	a	DET
ajst-25954	12	6	common	common	ADJ
ajst-25954	12	7	neurological	neurological	ADJ
ajst-25954	12	8	disorder	disorder	NOUN
ajst-25954	12	9	,	,	PUNCT
ajst-25954	12	10	epilepsy	epilepsy	NOUN
ajst-25954	12	11	is	be	AUX
ajst-25954	12	12	characterized	characterize	VERB
ajst-25954	12	13	by	by	ADP
ajst-25954	12	14	sudden	sudden	ADJ
ajst-25954	12	15	,	,	PUNCT
ajst-25954	12	16	abnormal	abnormal	ADJ
ajst-25954	12	17	and	and	CCONJ
ajst-25954	12	18	excessive	excessive	ADJ
ajst-25954	12	19	electrical	electrical	ADJ
ajst-25954	12	20	disturbance	disturbance	NOUN
ajst-25954	12	21	of	of	ADP
ajst-25954	12	22	brain	brain	NOUN
ajst-25954	12	23	neurons	neuron	NOUN
ajst-25954	12	24	.	.	PUNCT
ajst-25954	13	1	according	accord	VERB
ajst-25954	13	2	to	to	ADP
ajst-25954	13	3	the	the	DET
ajst-25954	13	4	world	world	PROPN
ajst-25954	13	5	health	health	NOUN
ajst-25954	13	6	organization	organization	NOUN
ajst-25954	13	7	,	,	PUNCT
ajst-25954	13	8	there	there	PRON
ajst-25954	13	9	are	be	VERB
ajst-25954	13	10	more	more	ADJ
ajst-25954	13	11	than	than	ADP
ajst-25954	13	12	50	50	NUM
ajst-25954	13	13	million	million	NUM
ajst-25954	13	14	patients	patient	NOUN
ajst-25954	13	15	with	with	ADP
ajst-25954	13	16	epilepsy	epilepsy	NOUN
ajst-25954	13	17	worldwide	worldwide	ADV
ajst-25954	13	18	[	[	X
ajst-25954	13	19	1	1	NUM
ajst-25954	13	20	]	]	PUNCT
ajst-25954	13	21	.	.	PUNCT
ajst-25954	14	1	electroencephalography	electroencephalography	NOUN
ajst-25954	14	2	(	(	PUNCT
ajst-25954	14	3	eeg	eeg	NOUN
ajst-25954	14	4	)	)	PUNCT
ajst-25954	14	5	has	have	AUX
ajst-25954	14	6	become	become	VERB
ajst-25954	14	7	an	an	DET
ajst-25954	14	8	indispensable	indispensable	ADJ
ajst-25954	14	9	tool	tool	NOUN
ajst-25954	14	10	in	in	ADP
ajst-25954	14	11	the	the	DET
ajst-25954	14	12	diagnosis	diagnosis	NOUN
ajst-25954	14	13	of	of	ADP
ajst-25954	14	14	brain	brain	NOUN
ajst-25954	14	15	diseases	disease	NOUN
ajst-25954	14	16	[	[	X
ajst-25954	14	17	2	2	NUM
ajst-25954	14	18	-	-	SYM
ajst-25954	14	19	3	3	NUM
ajst-25954	14	20	]	]	PUNCT
ajst-25954	14	21	.	.	PUNCT
ajst-25954	15	1	the	the	DET
ajst-25954	15	2	main	main	ADJ
ajst-25954	15	3	challenge	challenge	NOUN
ajst-25954	15	4	in	in	ADP
ajst-25954	15	5	the	the	DET
ajst-25954	15	6	face	face	NOUN
ajst-25954	15	7	of	of	ADP
ajst-25954	15	8	epilepsy	epilepsy	NOUN
ajst-25954	15	9	is	be	AUX
ajst-25954	15	10	the	the	DET
ajst-25954	15	11	inability	inability	NOUN
ajst-25954	15	12	to	to	PART
ajst-25954	15	13	predict	predict	VERB
ajst-25954	15	14	and	and	CCONJ
ajst-25954	15	15	control	control	VERB
ajst-25954	15	16	seizures	seizure	NOUN
ajst-25954	15	17	.	.	PUNCT
ajst-25954	16	1	if	if	SCONJ
ajst-25954	16	2	a	a	DET
ajst-25954	16	3	reliable	reliable	ADJ
ajst-25954	16	4	epilepsy	epilepsy	NOUN
ajst-25954	16	5	prediction	prediction	NOUN
ajst-25954	16	6	algorithm	algorithm	NOUN
ajst-25954	16	7	can	can	AUX
ajst-25954	16	8	be	be	AUX
ajst-25954	16	9	developed	develop	VERB
ajst-25954	16	10	to	to	PART
ajst-25954	16	11	capture	capture	VERB
ajst-25954	16	12	and	and	CCONJ
ajst-25954	16	13	identify	identify	VERB
ajst-25954	16	14	abnormal	abnormal	ADJ
ajst-25954	16	15	epileptic	epileptic	ADJ
ajst-25954	16	16	activity	activity	NOUN
ajst-25954	16	17	in	in	ADP
ajst-25954	16	18	the	the	DET
ajst-25954	16	19	clinic	clinic	NOUN
ajst-25954	16	20	[	[	X
ajst-25954	16	21	4	4	NUM
ajst-25954	16	22	]	]	PUNCT
ajst-25954	16	23	,	,	PUNCT
ajst-25954	16	24	effective	effective	ADJ
ajst-25954	16	25	measures	measure	NOUN
ajst-25954	16	26	can	can	AUX
ajst-25954	16	27	be	be	AUX
ajst-25954	16	28	taken	take	VERB
ajst-25954	16	29	in	in	ADP
ajst-25954	16	30	advance	advance	NOUN
ajst-25954	16	31	to	to	PART
ajst-25954	16	32	reduce	reduce	VERB
ajst-25954	16	33	the	the	DET
ajst-25954	16	34	harm	harm	NOUN
ajst-25954	16	35	caused	cause	VERB
ajst-25954	16	36	by	by	ADP
ajst-25954	16	37	avoiding	avoid	VERB
ajst-25954	16	38	seizures	seizure	NOUN
ajst-25954	16	39	.	.	PUNCT
ajst-25954	17	1	in	in	ADP
ajst-25954	17	2	view	view	NOUN
ajst-25954	17	3	of	of	ADP
ajst-25954	17	4	this	this	PRON
ajst-25954	17	5	,	,	PUNCT
ajst-25954	17	6	it	it	PRON
ajst-25954	17	7	is	be	AUX
ajst-25954	17	8	of	of	ADP
ajst-25954	17	9	great	great	ADJ
ajst-25954	17	10	practical	practical	ADJ
ajst-25954	17	11	value	value	NOUN
ajst-25954	17	12	to	to	PART
ajst-25954	17	13	develop	develop	VERB
ajst-25954	17	14	an	an	DET
ajst-25954	17	15	accurate	accurate	ADJ
ajst-25954	17	16	and	and	CCONJ
ajst-25954	17	17	reliable	reliable	ADJ
ajst-25954	17	18	epileptic	epileptic	ADJ
ajst-25954	17	19	seizure	seizure	NOUN
ajst-25954	17	20	prediction	prediction	NOUN
ajst-25954	17	21	system	system	NOUN
ajst-25954	17	22	to	to	PART
ajst-25954	17	23	reduce	reduce	VERB
ajst-25954	17	24	the	the	DET
ajst-25954	17	25	workload	workload	NOUN
ajst-25954	17	26	of	of	ADP
ajst-25954	17	27	medical	medical	ADJ
ajst-25954	17	28	personnel	personnel	NOUN
ajst-25954	17	29	.	.	PUNCT
ajst-25954	18	1	the	the	DET
ajst-25954	18	2	eeg	eeg	PROPN
ajst-25954	18	3	electrical	electrical	ADJ
ajst-25954	18	4	signal	signal	NOUN
ajst-25954	18	5	state	state	NOUN
ajst-25954	18	6	changed	change	VERB
ajst-25954	18	7	obviously	obviously	ADV
ajst-25954	18	8	before	before	ADV
ajst-25954	18	9	and	and	CCONJ
ajst-25954	18	10	after	after	ADP
ajst-25954	18	11	seizure	seizure	NOUN
ajst-25954	18	12	[	[	X
ajst-25954	18	13	5	5	NUM
ajst-25954	18	14	]	]	PUNCT
ajst-25954	18	15	.	.	PUNCT
ajst-25954	19	1	the	the	DET
ajst-25954	19	2	researchers	researcher	NOUN
ajst-25954	19	3	divided	divide	VERB
ajst-25954	19	4	this	this	DET
ajst-25954	19	5	seizure	seizure	NOUN
ajst-25954	19	6	process	process	NOUN
ajst-25954	19	7	into	into	ADP
ajst-25954	19	8	four	four	NUM
ajst-25954	19	9	periods	period	NOUN
ajst-25954	19	10	,	,	PUNCT
ajst-25954	19	11	including	include	VERB
ajst-25954	19	12	interictal	interictal	ADJ
ajst-25954	19	13	,	,	PUNCT
ajst-25954	19	14	preictal	preictal	ADJ
ajst-25954	19	15	,	,	PUNCT
ajst-25954	19	16	ictal	ictal	ADJ
ajst-25954	19	17	and	and	CCONJ
ajst-25954	19	18	postictal	postictal	NOUN
ajst-25954	19	19	.	.	PUNCT
ajst-25954	20	1	the	the	DET
ajst-25954	20	2	focus	focus	NOUN
ajst-25954	20	3	of	of	ADP
ajst-25954	20	4	seizures	seizure	NOUN
ajst-25954	20	5	is	be	AUX
ajst-25954	20	6	to	to	PART
ajst-25954	20	7	accurately	accurately	ADV
ajst-25954	20	8	identify	identify	VERB
ajst-25954	20	9	the	the	DET
ajst-25954	20	10	preictal	preictal	ADJ
ajst-25954	20	11	period	period	NOUN
ajst-25954	20	12	,	,	PUNCT
ajst-25954	20	13	by	by	ADP
ajst-25954	20	14	defining	define	VERB
ajst-25954	20	15	the	the	DET
ajst-25954	20	16	data	datum	NOUN
ajst-25954	20	17	length	length	NOUN
ajst-25954	20	18	of	of	ADP
ajst-25954	20	19	different	different	ADJ
ajst-25954	20	20	periods	period	NOUN
ajst-25954	20	21	,	,	PUNCT
ajst-25954	20	22	the	the	DET
ajst-25954	20	23	signals	signal	NOUN
ajst-25954	20	24	of	of	ADP
ajst-25954	20	25	the	the	DET
ajst-25954	20	26	interictal	interictal	ADJ
ajst-25954	20	27	period	period	NOUN
ajst-25954	20	28	and	and	CCONJ
ajst-25954	20	29	the	the	DET
ajst-25954	20	30	preictal	preictal	ADJ
ajst-25954	20	31	period	period	NOUN
ajst-25954	20	32	are	be	AUX
ajst-25954	20	33	distinguished	distinguish	VERB
ajst-25954	20	34	[	[	PUNCT
ajst-25954	20	35	6	6	NUM
ajst-25954	20	36	]	]	PUNCT
ajst-25954	20	37	.	.	PUNCT
ajst-25954	21	1	seizure	seizure	NOUN
ajst-25954	21	2	is	be	AUX
ajst-25954	21	3	the	the	DET
ajst-25954	21	4	result	result	NOUN
ajst-25954	21	5	of	of	ADP
ajst-25954	21	6	overactivity	overactivity	NOUN
ajst-25954	21	7	and	and	CCONJ
ajst-25954	21	8	abnormal	abnormal	ADJ
ajst-25954	21	9	activity	activity	NOUN
ajst-25954	21	10	of	of	ADP
ajst-25954	21	11	neurons	neuron	NOUN
ajst-25954	21	12	in	in	ADP
ajst-25954	21	13	the	the	DET
ajst-25954	21	14	cerebral	cerebral	ADJ
ajst-25954	21	15	cortex	cortex	NOUN
ajst-25954	21	16	,	,	PUNCT
ajst-25954	21	17	so	so	ADV
ajst-25954	21	18	epilepsy	epilepsy	NOUN
ajst-25954	21	19	is	be	AUX
ajst-25954	21	20	usually	usually	ADV
ajst-25954	21	21	detected	detect	VERB
ajst-25954	21	22	using	use	VERB
ajst-25954	21	23	eeg	eeg	PROPN
ajst-25954	21	24	.	.	PUNCT
ajst-25954	22	1	eeg	eeg	PROPN
ajst-25954	22	2	records	record	NOUN
ajst-25954	22	3	voltage	voltage	NOUN
ajst-25954	22	4	fluctuations	fluctuation	NOUN
ajst-25954	22	5	caused	cause	VERB
ajst-25954	22	6	by	by	ADP
ajst-25954	22	7	electrical	electrical	ADJ
ajst-25954	22	8	activity	activity	NOUN
ajst-25954	22	9	on	on	ADP
ajst-25954	22	10	the	the	DET
ajst-25954	22	11	scalp	scalp	NOUN
ajst-25954	22	12	surface	surface	NOUN
ajst-25954	22	13	.	.	PUNCT
ajst-25954	23	1	it	it	PRON
ajst-25954	23	2	helps	help	VERB
ajst-25954	23	3	detect	detect	VERB
ajst-25954	23	4	normal	normal	ADJ
ajst-25954	23	5	and	and	CCONJ
ajst-25954	23	6	abnormal	abnormal	ADJ
ajst-25954	23	7	activity	activity	NOUN
ajst-25954	23	8	occurring	occur	VERB
ajst-25954	23	9	in	in	ADP
ajst-25954	23	10	the	the	DET
ajst-25954	23	11	human	human	ADJ
ajst-25954	23	12	brain	brain	NOUN
ajst-25954	23	13	and	and	CCONJ
ajst-25954	23	14	shows	show	VERB
ajst-25954	23	15	the	the	DET
ajst-25954	23	16	dynamic	dynamic	ADJ
ajst-25954	23	17	changes	change	NOUN
ajst-25954	23	18	caused	cause	VERB
ajst-25954	23	19	by	by	ADP
ajst-25954	23	20	seizures	seizure	NOUN
ajst-25954	23	21	.	.	PUNCT
ajst-25954	24	1	with	with	ADP
ajst-25954	24	2	the	the	DET
ajst-25954	24	3	development	development	NOUN
ajst-25954	24	4	of	of	ADP
ajst-25954	24	5	technology	technology	NOUN
ajst-25954	24	6	,	,	PUNCT
ajst-25954	24	7	it	it	PRON
ajst-25954	24	8	has	have	AUX
ajst-25954	24	9	been	be	AUX
ajst-25954	24	10	widely	widely	ADV
ajst-25954	24	11	used	use	VERB
ajst-25954	24	12	in	in	ADP
ajst-25954	24	13	research	research	NOUN
ajst-25954	24	14	related	relate	VERB
ajst-25954	24	15	to	to	PART
ajst-25954	24	16	seizure	seizure	VERB
ajst-25954	24	17	prediction	prediction	NOUN
ajst-25954	24	18	[	[	X
ajst-25954	24	19	7	7	NUM
ajst-25954	24	20	-	-	SYM
ajst-25954	24	21	9	9	NUM
ajst-25954	24	22	]	]	PUNCT
ajst-25954	24	23	,	,	PUNCT
ajst-25954	24	24	and	and	CCONJ
ajst-25954	24	25	a	a	DET
ajst-25954	24	26	variety	variety	NOUN
ajst-25954	24	27	of	of	ADP
ajst-25954	24	28	seizure	seizure	NOUN
ajst-25954	24	29	prediction	prediction	NOUN
ajst-25954	24	30	methods	method	NOUN
ajst-25954	24	31	have	have	AUX
ajst-25954	24	32	been	be	AUX
ajst-25954	24	33	developed	develop	VERB
ajst-25954	24	34	.	.	PUNCT
ajst-25954	25	1	since	since	SCONJ
ajst-25954	25	2	the	the	DET
ajst-25954	25	3	development	development	NOUN
ajst-25954	25	4	of	of	ADP
ajst-25954	25	5	seizure	seizure	NOUN
ajst-25954	25	6	prediction	prediction	NOUN
ajst-25954	25	7	,	,	PUNCT
ajst-25954	25	8	there	there	PRON
ajst-25954	25	9	have	have	AUX
ajst-25954	25	10	been	be	AUX
ajst-25954	25	11	many	many	ADJ
ajst-25954	25	12	encouraging	encouraging	ADJ
ajst-25954	25	13	developments	development	NOUN
ajst-25954	25	14	in	in	ADP
ajst-25954	25	15	the	the	DET
ajst-25954	25	16	field	field	NOUN
ajst-25954	25	17	.	.	PUNCT
ajst-25954	26	1	by	by	ADP
ajst-25954	26	2	manually	manually	ADV
ajst-25954	26	3	extracting	extract	VERB
ajst-25954	26	4	features	feature	NOUN
ajst-25954	26	5	,	,	PUNCT
ajst-25954	26	6	it	it	PRON
ajst-25954	26	7	has	have	AUX
ajst-25954	26	8	made	make	VERB
ajst-25954	26	9	an	an	DET
ajst-25954	26	10	outstanding	outstanding	ADJ
ajst-25954	26	11	contribution	contribution	NOUN
ajst-25954	26	12	for	for	ADP
ajst-25954	26	13	the	the	DET
ajst-25954	26	14	field	field	NOUN
ajst-25954	26	15	of	of	ADP
ajst-25954	26	16	seizure	seizure	NOUN
ajst-25954	26	17	prediction	prediction	NOUN
ajst-25954	26	18	.	.	PUNCT
ajst-25954	27	1	lu	lu	PROPN
ajst-25954	27	2	et	et	PROPN
ajst-25954	27	3	al	al	PROPN
ajst-25954	27	4	.	.	PROPN
ajst-25954	27	5	proposed	propose	VERB
ajst-25954	27	6	an	an	DET
ajst-25954	27	7	automatic	automatic	ADJ
ajst-25954	27	8	classification	classification	NOUN
ajst-25954	27	9	method	method	NOUN
ajst-25954	27	10	for	for	ADP
ajst-25954	27	11	epilepsy	epilepsy	NOUN
ajst-25954	27	12	eeg	eeg	NOUN
ajst-25954	27	13	signals	signal	NOUN
ajst-25954	27	14	based	base	VERB
ajst-25954	27	15	on	on	ADP
ajst-25954	27	16	support	support	NOUN
ajst-25954	27	17	vector	vector	NOUN
ajst-25954	27	18	machines	machine	NOUN
ajst-25954	27	19	.	.	PUNCT
ajst-25954	28	1	they	they	PRON
ajst-25954	28	2	selected	select	VERB
ajst-25954	28	3	sample	sample	NOUN
ajst-25954	28	4	entropy	entropy	NOUN
ajst-25954	28	5	and	and	CCONJ
ajst-25954	28	6	higuchi	higuchi	PROPN
ajst-25954	28	7	fractal	fractal	ADJ
ajst-25954	28	8	dimension	dimension	NOUN
ajst-25954	28	9	as	as	ADP
ajst-25954	28	10	features	feature	NOUN
ajst-25954	28	11	,	,	PUNCT
ajst-25954	28	12	and	and	CCONJ
ajst-25954	28	13	the	the	DET
ajst-25954	28	14	nonlinear	nonlinear	ADJ
ajst-25954	28	15	features	feature	NOUN
ajst-25954	28	16	showed	show	VERB
ajst-25954	28	17	the	the	DET
ajst-25954	28	18	effectiveness	effectiveness	NOUN
ajst-25954	28	19	of	of	ADP
ajst-25954	28	20	seizure	seizure	NOUN
ajst-25954	28	21	prediction	prediction	NOUN
ajst-25954	28	22	[	[	X
ajst-25954	28	23	10	10	NUM
ajst-25954	28	24	]	]	PUNCT
ajst-25954	28	25	.	.	PUNCT
ajst-25954	29	1	yu	yu	PROPN
ajst-25954	29	2	et	et	PROPN
ajst-25954	29	3	al	al	PROPN
ajst-25954	29	4	.	.	PROPN
ajst-25954	29	5	combined	combine	VERB
ajst-25954	29	6	the	the	DET
ajst-25954	29	7	extracted	extract	VERB
ajst-25954	29	8	features	feature	NOUN
ajst-25954	29	9	with	with	ADP
ajst-25954	29	10	hidden	hidden	ADJ
ajst-25954	29	11	deep	deep	ADJ
ajst-25954	29	12	features	feature	NOUN
ajst-25954	29	13	in	in	ADP
ajst-25954	29	14	a	a	DET
ajst-25954	29	15	complementary	complementary	ADJ
ajst-25954	29	16	manner	manner	NOUN
ajst-25954	29	17	,	,	PUNCT
ajst-25954	29	18	which	which	PRON
ajst-25954	29	19	input	input	NOUN
ajst-25954	29	20	features	feature	VERB
ajst-25954	29	21	into	into	ADP
ajst-25954	29	22	multiplicative	multiplicative	ADJ
ajst-25954	29	23	long	long	ADJ
ajst-25954	29	24	short	short	ADJ
ajst-25954	29	25	-	-	PUNCT
ajst-25954	29	26	term	term	NOUN
ajst-25954	29	27	memory	memory	NOUN
ajst-25954	29	28	(	(	PUNCT
ajst-25954	29	29	mlstm	mlstm	PROPN
ajst-25954	29	30	)	)	PUNCT
ajst-25954	29	31	to	to	PART
ajst-25954	29	32	explore	explore	VERB
ajst-25954	29	33	the	the	DET
ajst-25954	29	34	temporal	temporal	ADJ
ajst-25954	29	35	dependence	dependence	NOUN
ajst-25954	29	36	of	of	ADP
ajst-25954	29	37	eeg	eeg	PROPN
ajst-25954	29	38	[	[	X
ajst-25954	29	39	11	11	NUM
ajst-25954	29	40	]	]	PUNCT
ajst-25954	29	41	.	.	PUNCT
ajst-25954	30	1	chen	chen	PROPN
ajst-25954	30	2	et	et	PROPN
ajst-25954	30	3	al	al	PROPN
ajst-25954	30	4	.	.	PROPN
ajst-25954	30	5	proposed	propose	VERB
ajst-25954	30	6	the	the	DET
ajst-25954	30	7	chaos	chaos	NOUN
ajst-25954	30	8	theory	theory	NOUN
ajst-25954	30	9	of	of	ADP
ajst-25954	30	10	seizure	seizure	NOUN
ajst-25954	30	11	detection	detection	NOUN
ajst-25954	30	12	combined	combine	VERB
ajst-25954	30	13	with	with	ADP
ajst-25954	30	14	decision	decision	NOUN
ajst-25954	30	15	tree	tree	NOUN
ajst-25954	30	16	[	[	X
ajst-25954	30	17	12	12	NUM
ajst-25954	30	18	]	]	PUNCT
ajst-25954	30	19	.	.	PUNCT
ajst-25954	31	1	the	the	DET
ajst-25954	31	2	theory	theory	NOUN
ajst-25954	31	3	often	often	ADV
ajst-25954	31	4	ignores	ignore	VERB
ajst-25954	31	5	the	the	DET
ajst-25954	31	6	correlation	correlation	NOUN
ajst-25954	31	7	of	of	ADP
ajst-25954	31	8	data	datum	NOUN
ajst-25954	31	9	attributes	attribute	NOUN
ajst-25954	31	10	.	.	PUNCT
ajst-25954	32	1	tapani	tapani	PROPN
ajst-25954	32	2	et	et	PROPN
ajst-25954	32	3	al	al	PROPN
ajst-25954	32	4	.	.	PROPN
ajst-25954	32	5	proposed	propose	VERB
ajst-25954	32	6	a	a	DET
ajst-25954	32	7	new	new	ADJ
ajst-25954	32	8	method	method	NOUN
ajst-25954	32	9	to	to	PART
ajst-25954	32	10	detect	detect	VERB
ajst-25954	32	11	neonatal	neonatal	ADJ
ajst-25954	32	12	epilepsy	epilepsy	NOUN
ajst-25954	32	13	by	by	ADP
ajst-25954	32	14	extracting	extract	VERB
ajst-25954	32	15	non	non	ADJ
ajst-25954	32	16	-	-	ADJ
ajst-25954	32	17	stationary	stationary	ADJ
ajst-25954	32	18	periodic	periodic	ADJ
ajst-25954	32	19	features	feature	NOUN
ajst-25954	32	20	in	in	ADP
ajst-25954	32	21	the	the	DET
ajst-25954	32	22	time	time	NOUN
ajst-25954	32	23	and	and	CCONJ
ajst-25954	32	24	frequency	frequency	NOUN
ajst-25954	32	25	domains	domain	NOUN
ajst-25954	32	26	[	[	X
ajst-25954	32	27	13	13	NUM
ajst-25954	32	28	]	]	PUNCT
ajst-25954	32	29	.	.	PUNCT
ajst-25954	33	1	feature	feature	NOUN
ajst-25954	33	2	extraction	extraction	NOUN
ajst-25954	33	3	enables	enable	VERB
ajst-25954	33	4	the	the	DET
ajst-25954	33	5	model	model	NOUN
ajst-25954	33	6	to	to	PART
ajst-25954	33	7	capture	capture	VERB
ajst-25954	33	8	the	the	DET
ajst-25954	33	9	basic	basic	ADJ
ajst-25954	33	10	features	feature	NOUN
ajst-25954	33	11	of	of	ADP
ajst-25954	33	12	eeg	eeg	PROPN
ajst-25954	33	13	data	datum	NOUN
ajst-25954	33	14	,	,	PUNCT
ajst-25954	33	15	and	and	CCONJ
ajst-25954	33	16	appropriate	appropriate	ADJ
ajst-25954	33	17	feature	feature	NOUN
ajst-25954	33	18	selection	selection	NOUN
ajst-25954	33	19	determines	determine	VERB
ajst-25954	33	20	the	the	DET
ajst-25954	33	21	accuracy	accuracy	NOUN
ajst-25954	33	22	of	of	ADP
ajst-25954	33	23	the	the	DET
ajst-25954	33	24	system	system	NOUN
ajst-25954	33	25	.	.	PUNCT
ajst-25954	34	1	however	however	ADV
ajst-25954	34	2	,	,	PUNCT
ajst-25954	34	3	highly	highly	ADV
ajst-25954	34	4	redundant	redundant	ADJ
ajst-25954	34	5	features	feature	NOUN
ajst-25954	34	6	can	can	AUX
ajst-25954	34	7	affect	affect	VERB
ajst-25954	34	8	the	the	DET
ajst-25954	34	9	performance	performance	NOUN
ajst-25954	34	10	of	of	ADP
ajst-25954	34	11	the	the	DET
ajst-25954	34	12	model	model	NOUN
ajst-25954	34	13	,	,	PUNCT
ajst-25954	34	14	and	and	CCONJ
ajst-25954	34	15	these	these	DET
ajst-25954	34	16	methods	method	NOUN
ajst-25954	34	17	do	do	AUX
ajst-25954	34	18	not	not	PART
ajst-25954	34	19	effectively	effectively	ADV
ajst-25954	34	20	deal	deal	VERB
ajst-25954	34	21	with	with	ADP
ajst-25954	34	22	the	the	DET
ajst-25954	34	23	association	association	NOUN
ajst-25954	34	24	between	between	ADP
ajst-25954	34	25	features	feature	NOUN
ajst-25954	34	26	.	.	PUNCT
ajst-25954	35	1	only	only	ADV
ajst-25954	35	2	by	by	ADP
ajst-25954	35	3	selecting	select	VERB
ajst-25954	35	4	features	feature	NOUN
ajst-25954	35	5	to	to	PART
ajst-25954	35	6	reduce	reduce	VERB
ajst-25954	35	7	the	the	DET
ajst-25954	35	8	redundancy	redundancy	NOUN
ajst-25954	35	9	among	among	ADP
ajst-25954	35	10	features	feature	NOUN
ajst-25954	35	11	,	,	PUNCT
ajst-25954	35	12	it	it	PRON
ajst-25954	35	13	can	can	AUX
ajst-25954	35	14	improve	improve	VERB
ajst-25954	35	15	the	the	DET
ajst-25954	35	16	computational	computational	ADJ
ajst-25954	35	17	efficiency	efficiency	NOUN
ajst-25954	35	18	of	of	ADP
ajst-25954	35	19	the	the	DET
ajst-25954	35	20	model	model	NOUN
ajst-25954	35	21	,	,	PUNCT
ajst-25954	35	22	which	which	PRON
ajst-25954	35	23	the	the	DET
ajst-25954	35	24	performance	performance	NOUN
ajst-25954	35	25	of	of	ADP
ajst-25954	35	26	the	the	DET
ajst-25954	35	27	model	model	NOUN
ajst-25954	35	28	be	be	AUX
ajst-25954	35	29	optimized	optimize	VERB
ajst-25954	35	30	.	.	PUNCT
ajst-25954	36	1	with	with	ADP
ajst-25954	36	2	the	the	DET
ajst-25954	36	3	rapid	rapid	ADJ
ajst-25954	36	4	development	development	NOUN
ajst-25954	36	5	of	of	ADP
ajst-25954	36	6	neural	neural	ADJ
ajst-25954	36	7	networks	network	NOUN
ajst-25954	36	8	,	,	PUNCT
ajst-25954	36	9	a	a	DET
ajst-25954	36	10	variety	variety	NOUN
ajst-25954	36	11	of	of	ADP
ajst-25954	36	12	deep	deep	ADJ
ajst-25954	36	13	learning	learn	VERB
ajst-25954	36	14	neural	neural	ADJ
ajst-25954	36	15	networks	network	NOUN
ajst-25954	36	16	combined	combine	VERB
ajst-25954	36	17	with	with	ADP
ajst-25954	36	18	attention	attention	NOUN
ajst-25954	36	19	mechanisms	mechanism	NOUN
ajst-25954	36	20	are	be	AUX
ajst-25954	36	21	applied	apply	VERB
ajst-25954	36	22	in	in	ADP
ajst-25954	36	23	seizure	seizure	NOUN
ajst-25954	36	24	prediction	prediction	NOUN
ajst-25954	36	25	.	.	PUNCT
ajst-25954	37	1	among	among	ADP
ajst-25954	37	2	them	they	PRON
ajst-25954	37	3	,	,	PUNCT
ajst-25954	37	4	the	the	DET
ajst-25954	37	5	classic	classic	ADJ
ajst-25954	37	6	ones	one	NOUN
ajst-25954	37	7	are	be	AUX
ajst-25954	37	8	recurrent	recurrent	ADJ
ajst-25954	37	9	neural	neural	ADJ
ajst-25954	37	10	networks	network	NOUN
ajst-25954	37	11	(	(	PUNCT
ajst-25954	37	12	rnn	rnn	PROPN
ajst-25954	37	13	)	)	PUNCT
ajst-25954	37	14	,	,	PUNCT
ajst-25954	37	15	convolutional	convolutional	ADJ
ajst-25954	37	16	neural	neural	ADJ
ajst-25954	37	17	networks	network	NOUN
ajst-25954	37	18	(	(	PUNCT
ajst-25954	37	19	cnn	cnn	PROPN
ajst-25954	37	20	)	)	PUNCT
ajst-25954	37	21	and	and	CCONJ
ajst-25954	37	22	long	long	ADJ
ajst-25954	37	23	short	short	ADJ
ajst-25954	37	24	-	-	PUNCT
ajst-25954	37	25	term	term	NOUN
ajst-25954	37	26	memory	memory	NOUN
ajst-25954	37	27	networks	network	NOUN
ajst-25954	37	28	(	(	PUNCT
ajst-25954	37	29	lstm	lstm	NOUN
ajst-25954	37	30	)	)	PUNCT
ajst-25954	37	31	.	.	PUNCT
ajst-25954	38	1	he	he	PRON
ajst-25954	38	2	et	et	PROPN
ajst-25954	38	3	al	al	PROPN
ajst-25954	38	4	.	.	PROPN
ajst-25954	38	5	employed	employ	VERB
ajst-25954	38	6	graph	graph	NOUN
ajst-25954	38	7	attention	attention	NOUN
ajst-25954	38	8	network	network	NOUN
ajst-25954	38	9	as	as	ADP
ajst-25954	38	10	the	the	DET
ajst-25954	38	11	front	front	ADJ
ajst-25954	38	12	end	end	NOUN
ajst-25954	38	13	to	to	PART
ajst-25954	38	14	extract	extract	VERB
ajst-25954	38	15	spatial	spatial	ADJ
ajst-25954	38	16	features	feature	NOUN
ajst-25954	38	17	,	,	PUNCT
ajst-25954	38	18	82	82	NUM
ajst-25954	38	19	and	and	CCONJ
ajst-25954	38	20	bidirectional	bidirectional	ADJ
ajst-25954	38	21	long	long	ADJ
ajst-25954	38	22	short	short	ADJ
ajst-25954	38	23	-	-	PUNCT
ajst-25954	38	24	term	term	NOUN
ajst-25954	38	25	memory	memory	NOUN
ajst-25954	38	26	(	(	PUNCT
ajst-25954	38	27	bi	bi	NOUN
ajst-25954	38	28	-	-	ADJ
ajst-25954	38	29	lstm	lstm	ADJ
ajst-25954	38	30	)	)	PUNCT
ajst-25954	38	31	as	as	ADP
ajst-25954	38	32	the	the	DET
ajst-25954	38	33	back	back	ADJ
ajst-25954	38	34	end	end	NOUN
ajst-25954	38	35	to	to	PART
ajst-25954	38	36	capture	capture	VERB
ajst-25954	38	37	temporal	temporal	ADJ
ajst-25954	38	38	relationships	relationship	NOUN
ajst-25954	38	39	[	[	X
ajst-25954	38	40	14	14	NUM
ajst-25954	38	41	]	]	PUNCT
ajst-25954	38	42	.	.	PUNCT
ajst-25954	39	1	ra	ra	PROPN
ajst-25954	39	2	et	et	PROPN
ajst-25954	39	3	al	al	PROPN
ajst-25954	39	4	.	.	PROPN
ajst-25954	39	5	improved	improve	VERB
ajst-25954	39	6	the	the	DET
ajst-25954	39	7	time	time	NOUN
ajst-25954	39	8	-	-	PUNCT
ajst-25954	39	9	frequency	frequency	NOUN
ajst-25954	39	10	resolution	resolution	NOUN
ajst-25954	39	11	of	of	ADP
ajst-25954	39	12	eeg	eeg	NOUN
ajst-25954	39	13	features	feature	NOUN
ajst-25954	39	14	based	base	VERB
ajst-25954	39	15	on	on	ADP
ajst-25954	39	16	singular	singular	ADJ
ajst-25954	39	17	value	value	NOUN
ajst-25954	39	18	decomposition	decomposition	NOUN
ajst-25954	39	19	(	(	PUNCT
ajst-25954	39	20	set	set	NOUN
ajst-25954	39	21	-	-	PUNCT
ajst-25954	39	22	svd	svd	PROPN
ajst-25954	39	23	)	)	PUNCT
ajst-25954	39	24	,	,	PUNCT
ajst-25954	39	25	and	and	CCONJ
ajst-25954	39	26	then	then	ADV
ajst-25954	39	27	used	use	VERB
ajst-25954	39	28	cnn	cnn	PROPN
ajst-25954	39	29	to	to	PART
ajst-25954	39	30	classify	classify	VERB
ajst-25954	39	31	seizure	seizure	NOUN
ajst-25954	39	32	states	state	NOUN
ajst-25954	39	33	[	[	X
ajst-25954	39	34	15	15	NUM
ajst-25954	39	35	]	]	PUNCT
ajst-25954	39	36	.	.	PUNCT
ajst-25954	40	1	sun	sun	PROPN
ajst-25954	40	2	et	et	PROPN
ajst-25954	40	3	al	al	PROPN
ajst-25954	40	4	.	.	PROPN
ajst-25954	40	5	adopted	adopt	VERB
ajst-25954	40	6	time	time	NOUN
ajst-25954	40	7	  	  	SPACE
ajst-25954	40	8	domain	domain	NOUN
ajst-25954	40	9	  	  	SPACE
ajst-25954	40	10	analysis	analysis	NOUN
ajst-25954	40	11	mapping	mapping	NOUN
ajst-25954	40	12	1×1	1×1	NOUN
ajst-25954	40	13	   	   	SPACE
ajst-25954	40	14	conv1	conv1	NOUN
ajst-25954	40	15	3×3	3×3	NUM
ajst-25954	40	16	   	   	SPACE
ajst-25954	40	17	conv2	conv2	NOUN
ajst-25954	40	18	5×5	5×5	NUM
ajst-25954	40	19	   	   	SPACE
ajst-25954	40	20	conv3	conv3	NOUN
ajst-25954	40	21	7×7	7×7	NOUN
ajst-25954	40	22	   	   	SPACE
ajst-25954	40	23	conv4	conv4	NOUN
ajst-25954	40	24	5×5	5×5	NUM
ajst-25954	40	25	   	   	SPACE
ajst-25954	40	26	conv5	conv5	NOUN
ajst-25954	40	27	c	c	NOUN
ajst-25954	40	28	on	on	ADP
ajst-25954	40	29	catflatten	catflatten	PROPN
ajst-25954	40	30	...	...	PUNCT
ajst-25954	40	31	...	...	PUNCT
ajst-25954	40	32	...	...	PUNCT
ajst-25954	40	33	...	...	PUNCT
ajst-25954	41	1	eca	eca	NOUN
ajst-25954	41	2	  	  	SPACE
ajst-25954	41	3	attention	attention	PROPN
ajst-25954	41	4	fm	fm	PROPN
ajst-25954	41	5	 	 	SPACE
ajst-25954	41	6	layer	layer	NOUN
ajst-25954	41	7	mlp	mlp	NOUN
ajst-25954	41	8	 	 	SPACE
ajst-25954	41	9	layer	layer	NOUN
ajst-25954	41	10	conv	conv	PROPN
ajst-25954	41	11	 	 	SPACE
ajst-25954	41	12	layer	layer	NOUN
ajst-25954	41	13	feature	feature	NOUN
ajst-25954	41	14	 	 	SPACE
ajst-25954	41	15	matrix	matrix	NOUN
ajst-25954	41	16	classification	classification	NOUN
ajst-25954	41	17	preictal	preictal	ADJ
ajst-25954	41	18	interictal	interictal	ADJ
ajst-25954	41	19	softmax	softmax	NOUN
ajst-25954	41	20	figure	figure	NOUN
ajst-25954	41	21	1	1	NUM
ajst-25954	41	22	.	.	PUNCT
ajst-25954	41	23	seizure	seizure	VERB
ajst-25954	41	24	prediction	prediction	NOUN
ajst-25954	41	25	framework	framework	NOUN
ajst-25954	41	26	of	of	ADP
ajst-25954	41	27	ms	ms	PROPN
ajst-25954	41	28	-	-	PUNCT
ajst-25954	41	29	stfm	stfm	NOUN
ajst-25954	41	30	-	-	PUNCT
ajst-25954	41	31	pcfwnet	pcfwnet	NOUN
ajst-25954	41	32	the	the	DET
ajst-25954	41	33	method	method	NOUN
ajst-25954	41	34	of	of	ADP
ajst-25954	41	35	channel	channel	NOUN
ajst-25954	41	36	attention	attention	NOUN
ajst-25954	41	37	to	to	PART
ajst-25954	41	38	fuse	fuse	VERB
ajst-25954	41	39	temporal	temporal	ADJ
ajst-25954	41	40	and	and	CCONJ
ajst-25954	41	41	spatial	spatial	ADJ
ajst-25954	41	42	features	feature	NOUN
ajst-25954	41	43	with	with	ADP
ajst-25954	41	44	original	original	ADJ
ajst-25954	41	45	eeg	eeg	NOUN
ajst-25954	41	46	data	datum	NOUN
ajst-25954	41	47	,	,	PUNCT
ajst-25954	41	48	so	so	SCONJ
ajst-25954	41	49	as	as	SCONJ
ajst-25954	41	50	to	to	PART
ajst-25954	41	51	acquire	acquire	VERB
ajst-25954	41	52	timing	timing	NOUN
ajst-25954	41	53	information	information	NOUN
ajst-25954	41	54	,	,	PUNCT
ajst-25954	41	55	spectrum	spectrum	VERB
ajst-25954	41	56	information	information	NOUN
ajst-25954	41	57	and	and	CCONJ
ajst-25954	41	58	spatial	spatial	ADJ
ajst-25954	41	59	location	location	NOUN
ajst-25954	41	60	information	information	NOUN
ajst-25954	41	61	in	in	ADP
ajst-25954	41	62	eeg	eeg	PROPN
ajst-25954	41	63	data	datum	NOUN
ajst-25954	41	64	[	[	X
ajst-25954	41	65	16	16	NUM
ajst-25954	41	66	]	]	PUNCT
ajst-25954	41	67	.	.	PUNCT
ajst-25954	42	1	due	due	ADP
ajst-25954	42	2	to	to	ADP
ajst-25954	42	3	its	its	PRON
ajst-25954	42	4	conventional	conventional	ADJ
ajst-25954	42	5	convolution	convolution	NOUN
ajst-25954	42	6	operation	operation	NOUN
ajst-25954	42	7	and	and	CCONJ
ajst-25954	42	8	local	local	ADJ
ajst-25954	42	9	receptor	receptor	NOUN
ajst-25954	42	10	field	field	NOUN
ajst-25954	42	11	,	,	PUNCT
ajst-25954	42	12	the	the	DET
ajst-25954	42	13	cnn	cnn	PROPN
ajst-25954	42	14	framework	framework	NOUN
ajst-25954	42	15	in	in	ADP
ajst-25954	42	16	the	the	DET
ajst-25954	42	17	epilepsy	epilepsy	NOUN
ajst-25954	42	18	prediction	prediction	NOUN
ajst-25954	42	19	task	task	NOUN
ajst-25954	42	20	can	can	AUX
ajst-25954	42	21	only	only	ADV
ajst-25954	42	22	learn	learn	VERB
ajst-25954	42	23	lowdimensional	lowdimensional	ADJ
ajst-25954	42	24	spatial	spatial	ADJ
ajst-25954	42	25	correlations	correlation	NOUN
ajst-25954	42	26	between	between	ADP
ajst-25954	42	27	eeg	eeg	NOUN
ajst-25954	42	28	channels	channel	NOUN
ajst-25954	42	29	[	[	X
ajst-25954	42	30	17	17	NUM
ajst-25954	42	31	]	]	PUNCT
ajst-25954	42	32	.	.	PUNCT
ajst-25954	43	1	therefore	therefore	ADV
ajst-25954	43	2	,	,	PUNCT
ajst-25954	43	3	graph	graph	VERB
ajst-25954	43	4	convolutional	convolutional	ADJ
ajst-25954	43	5	networks	network	NOUN
ajst-25954	43	6	(	(	PUNCT
ajst-25954	43	7	gcn	gcn	NOUN
ajst-25954	43	8	)	)	PUNCT
ajst-25954	43	9	are	be	AUX
ajst-25954	43	10	studied	study	VERB
ajst-25954	43	11	in	in	ADP
ajst-25954	43	12	the	the	DET
ajst-25954	43	13	latest	late	ADJ
ajst-25954	43	14	research	research	NOUN
ajst-25954	43	15	[	[	X
ajst-25954	43	16	18	18	NUM
ajst-25954	43	17	]	]	PUNCT
ajst-25954	43	18	,	,	PUNCT
ajst-25954	43	19	dissanayake	dissanayake	PROPN
ajst-25954	43	20	et	et	PROPN
ajst-25954	43	21	al	al	PROPN
ajst-25954	43	22	.	.	PROPN
ajst-25954	43	23	proposed	propose	VERB
ajst-25954	43	24	a	a	DET
ajst-25954	43	25	subject	subject	ADJ
ajst-25954	43	26	-	-	PUNCT
ajst-25954	43	27	independent	independent	ADJ
ajst-25954	43	28	epileptic	epileptic	ADJ
ajst-25954	43	29	seizure	seizure	NOUN
ajst-25954	43	30	predictor	predictor	NOUN
ajst-25954	43	31	,	,	PUNCT
ajst-25954	43	32	the	the	DET
ajst-25954	43	33	prediction	prediction	NOUN
ajst-25954	43	34	of	of	ADP
ajst-25954	43	35	seizure	seizure	NOUN
ajst-25954	43	36	is	be	AUX
ajst-25954	43	37	employed	employ	VERB
ajst-25954	43	38	by	by	ADP
ajst-25954	43	39	using	use	VERB
ajst-25954	43	40	geometric	geometric	ADJ
ajst-25954	43	41	deep	deep	ADJ
ajst-25954	43	42	learning	learning	NOUN
ajst-25954	43	43	to	to	PART
ajst-25954	43	44	synthesize	synthesize	VERB
ajst-25954	43	45	eeg	eeg	NOUN
ajst-25954	43	46	through	through	ADP
ajst-25954	43	47	lstm	lstm	NOUN
ajst-25954	43	48	network	network	NOUN
ajst-25954	43	49	[	[	X
ajst-25954	43	50	19	19	NUM
ajst-25954	43	51	]	]	PUNCT
ajst-25954	43	52	.	.	PUNCT
ajst-25954	44	1	a	a	DET
ajst-25954	44	2	common	common	ADJ
ajst-25954	44	3	procedure	procedure	NOUN
ajst-25954	44	4	in	in	ADP
ajst-25954	44	5	gcn	gcn	NOUN
ajst-25954	44	6	is	be	AUX
ajst-25954	44	7	to	to	PART
ajst-25954	44	8	define	define	VERB
ajst-25954	44	9	a	a	DET
ajst-25954	44	10	prior	prior	ADJ
ajst-25954	44	11	adjacency	adjacency	NOUN
ajst-25954	44	12	matrix	matrix	NOUN
ajst-25954	44	13	to	to	PART
ajst-25954	44	14	build	build	VERB
ajst-25954	44	15	the	the	DET
ajst-25954	44	16	graph	graph	NOUN
ajst-25954	44	17	structure	structure	NOUN
ajst-25954	44	18	between	between	ADP
ajst-25954	44	19	channels	channel	NOUN
ajst-25954	44	20	,	,	PUNCT
ajst-25954	44	21	which	which	PRON
ajst-25954	44	22	helps	help	VERB
ajst-25954	44	23	to	to	PART
ajst-25954	44	24	convert	convert	VERB
ajst-25954	44	25	epileptic	epileptic	ADJ
ajst-25954	44	26	eeg	eeg	NOUN
ajst-25954	44	27	signals	signal	NOUN
ajst-25954	44	28	into	into	ADP
ajst-25954	44	29	a	a	DET
ajst-25954	44	30	graph	graph	NOUN
ajst-25954	44	31	representation	representation	NOUN
ajst-25954	44	32	with	with	ADP
ajst-25954	44	33	graph	graph	NOUN
ajst-25954	44	34	nodes	node	NOUN
ajst-25954	44	35	and	and	CCONJ
ajst-25954	44	36	edges	edge	NOUN
ajst-25954	44	37	[	[	X
ajst-25954	44	38	20	20	NUM
ajst-25954	44	39	]	]	PUNCT
ajst-25954	44	40	.	.	PUNCT
ajst-25954	45	1	zhong	zhong	PROPN
ajst-25954	45	2	et	et	PROPN
ajst-25954	45	3	al	al	PROPN
ajst-25954	45	4	.	.	PROPN
ajst-25954	45	5	introduced	introduce	VERB
ajst-25954	45	6	the	the	DET
ajst-25954	45	7	differential	differential	ADJ
ajst-25954	45	8	entropy	entropy	NOUN
ajst-25954	45	9	(	(	PUNCT
ajst-25954	45	10	de	de	NOUN
ajst-25954	45	11	)	)	PUNCT
ajst-25954	45	12	to	to	ADP
ajst-25954	45	13	inference	inference	NOUN
ajst-25954	45	14	of	of	ADP
ajst-25954	45	15	spatial	spatial	NOUN
ajst-25954	45	16	coupled	couple	VERB
ajst-25954	45	17	in	in	ADP
ajst-25954	45	18	network	network	NOUN
ajst-25954	45	19	topology	topology	NOUN
ajst-25954	45	20	,	,	PUNCT
ajst-25954	45	21	which	which	PRON
ajst-25954	45	22	calculated	calculate	VERB
ajst-25954	45	23	the	the	DET
ajst-25954	45	24	temporal	temporal	ADJ
ajst-25954	45	25	correlation	correlation	NOUN
ajst-25954	45	26	of	of	ADP
ajst-25954	45	27	eeg	eeg	PROPN
ajst-25954	45	28	and	and	CCONJ
ajst-25954	45	29	generated	generate	VERB
ajst-25954	45	30	graph	graph	NOUN
ajst-25954	45	31	nodes	node	NOUN
ajst-25954	45	32	[	[	X
ajst-25954	45	33	21	21	NUM
ajst-25954	45	34	]	]	PUNCT
ajst-25954	45	35	.	.	PUNCT
ajst-25954	46	1	li	li	PROPN
ajst-25954	46	2	et	et	PROPN
ajst-25954	46	3	al	al	PROPN
ajst-25954	46	4	.	.	PROPN
ajst-25954	46	5	proposed	propose	VERB
ajst-25954	46	6	a	a	DET
ajst-25954	46	7	time	time	NOUN
ajst-25954	46	8	-	-	PUNCT
ajst-25954	46	9	spectrum	spectrum	NOUN
ajst-25954	46	10	compression	compression	NOUN
ajst-25954	46	11	and	and	CCONJ
ajst-25954	46	12	excitation	excitation	NOUN
ajst-25954	46	13	scheme	scheme	NOUN
ajst-25954	46	14	,	,	PUNCT
ajst-25954	46	15	which	which	PRON
ajst-25954	46	16	can	can	AUX
ajst-25954	46	17	effectively	effectively	ADV
ajst-25954	46	18	integrate	integrate	VERB
ajst-25954	46	19	the	the	DET
ajst-25954	46	20	layered	layered	ADJ
ajst-25954	46	21	multi	multi	ADJ
ajst-25954	46	22	-	-	ADJ
ajst-25954	46	23	domain	domain	ADJ
ajst-25954	46	24	representation	representation	NOUN
ajst-25954	46	25	of	of	ADP
ajst-25954	46	26	eeg	eeg	PROPN
ajst-25954	46	27	for	for	ADP
ajst-25954	46	28	epilepsy	epilepsy	NOUN
ajst-25954	46	29	,	,	PUNCT
ajst-25954	46	30	thereby	thereby	ADV
ajst-25954	46	31	reducing	reduce	VERB
ajst-25954	46	32	the	the	DET
ajst-25954	46	33	information	information	NOUN
ajst-25954	46	34	redundancy	redundancy	NOUN
ajst-25954	46	35	of	of	ADP
ajst-25954	46	36	high	high	ADJ
ajst-25954	46	37	-	-	PUNCT
ajst-25954	46	38	dimensional	dimensional	ADJ
ajst-25954	46	39	features[22	features[22	NOUN
ajst-25954	46	40	]	]	PUNCT
ajst-25954	46	41	.	.	PUNCT
ajst-25954	47	1	prathaban	prathaban	PROPN
ajst-25954	47	2	et	et	PROPN
ajst-25954	47	3	al	al	PROPN
ajst-25954	47	4	.	.	PROPN
ajst-25954	47	5	reconstructed	reconstruct	VERB
ajst-25954	47	6	eeg	eeg	NOUN
ajst-25954	47	7	with	with	ADP
ajst-25954	47	8	sparsity	sparsity	NOUN
ajst-25954	47	9	and	and	CCONJ
ajst-25954	47	10	converted	convert	VERB
ajst-25954	47	11	it	it	PRON
ajst-25954	47	12	into	into	ADP
ajst-25954	47	13	a	a	DET
ajst-25954	47	14	twodimensional	twodimensional	ADJ
ajst-25954	47	15	(	(	PUNCT
ajst-25954	47	16	2d	2d	NOUN
ajst-25954	47	17	)	)	PUNCT
ajst-25954	47	18	image	image	NOUN
ajst-25954	47	19	.	.	PUNCT
ajst-25954	48	1	the	the	DET
ajst-25954	48	2	time	time	NOUN
ajst-25954	48	3	,	,	PUNCT
ajst-25954	48	4	signal	signal	NOUN
ajst-25954	48	5	value	value	NOUN
ajst-25954	48	6	and	and	CCONJ
ajst-25954	48	7	channel	channel	NOUN
ajst-25954	48	8	representation	representation	NOUN
ajst-25954	48	9	of	of	ADP
ajst-25954	48	10	2d	2d	NOUN
ajst-25954	48	11	images	image	NOUN
ajst-25954	48	12	are	be	AUX
ajst-25954	48	13	converted	convert	VERB
ajst-25954	48	14	to	to	ADP
ajst-25954	48	15	threedimensional	threedimensional	ADJ
ajst-25954	48	16	(	(	PUNCT
ajst-25954	48	17	3d	3d	NOUN
ajst-25954	48	18	)	)	PUNCT
ajst-25954	48	19	images	image	NOUN
ajst-25954	48	20	to	to	PART
ajst-25954	48	21	interpret	interpret	VERB
ajst-25954	48	22	the	the	DET
ajst-25954	48	23	relationship	relationship	NOUN
ajst-25954	48	24	between	between	ADP
ajst-25954	48	25	channels	channel	NOUN
ajst-25954	48	26	,	,	PUNCT
ajst-25954	48	27	and	and	CCONJ
ajst-25954	48	28	the	the	DET
ajst-25954	48	29	optimized	optimize	VERB
ajst-25954	48	30	3d	3d	NUM
ajst-25954	48	31	convolutional	convolutional	ADJ
ajst-25954	48	32	neural	neural	ADJ
ajst-25954	48	33	network	network	NOUN
ajst-25954	48	34	is	be	AUX
ajst-25954	48	35	used	use	VERB
ajst-25954	48	36	to	to	PART
ajst-25954	48	37	predict	predict	VERB
ajst-25954	48	38	seizures	seizure	NOUN
ajst-25954	48	39	[	[	X
ajst-25954	48	40	23	23	NUM
ajst-25954	48	41	]	]	PUNCT
ajst-25954	48	42	,	,	PUNCT
ajst-25954	48	43	the	the	DET
ajst-25954	48	44	feasibility	feasibility	NOUN
ajst-25954	48	45	based	base	VERB
ajst-25954	48	46	on	on	ADP
ajst-25954	48	47	3d	3d	NUM
ajst-25954	48	48	neural	neural	ADJ
ajst-25954	48	49	network	network	NOUN
ajst-25954	48	50	is	be	AUX
ajst-25954	48	51	demonstrated	demonstrate	VERB
ajst-25954	48	52	.	.	PUNCT
ajst-25954	49	1	however	however	ADV
ajst-25954	49	2	,	,	PUNCT
ajst-25954	49	3	in	in	ADP
ajst-25954	49	4	their	their	PRON
ajst-25954	49	5	dependence	dependence	NOUN
ajst-25954	49	6	on	on	ADP
ajst-25954	49	7	capturing	capture	VERB
ajst-25954	49	8	longer	long	ADJ
ajst-25954	49	9	sequences	sequence	NOUN
ajst-25954	49	10	,	,	PUNCT
ajst-25954	49	11	these	these	DET
ajst-25954	49	12	models	model	NOUN
ajst-25954	49	13	usually	usually	ADV
ajst-25954	49	14	ignore	ignore	VERB
ajst-25954	49	15	information	information	NOUN
ajst-25954	49	16	about	about	ADP
ajst-25954	49	17	the	the	DET
ajst-25954	49	18	spatial	spatial	ADJ
ajst-25954	49	19	location	location	NOUN
ajst-25954	49	20	of	of	ADP
ajst-25954	49	21	electrodes	electrode	NOUN
ajst-25954	49	22	in	in	ADP
ajst-25954	49	23	brain	brain	NOUN
ajst-25954	49	24	regions	region	NOUN
ajst-25954	49	25	with	with	ADP
ajst-25954	49	26	multiple	multiple	ADJ
ajst-25954	49	27	channels	channel	NOUN
ajst-25954	49	28	.	.	PUNCT
ajst-25954	50	1	this	this	DET
ajst-25954	50	2	information	information	NOUN
ajst-25954	50	3	may	may	AUX
ajst-25954	50	4	contain	contain	VERB
ajst-25954	50	5	valuable	valuable	ADJ
ajst-25954	50	6	information	information	NOUN
ajst-25954	50	7	for	for	ADP
ajst-25954	50	8	predicting	predict	VERB
ajst-25954	50	9	seizures	seizure	NOUN
ajst-25954	50	10	.	.	PUNCT
ajst-25954	51	1	the	the	DET
ajst-25954	51	2	main	main	ADJ
ajst-25954	51	3	purpose	purpose	NOUN
ajst-25954	51	4	of	of	ADP
ajst-25954	51	5	this	this	DET
ajst-25954	51	6	paper	paper	NOUN
ajst-25954	51	7	is	be	AUX
ajst-25954	51	8	to	to	PART
ajst-25954	51	9	break	break	VERB
ajst-25954	51	10	through	through	ADP
ajst-25954	51	11	the	the	DET
ajst-25954	51	12	limitations	limitation	NOUN
ajst-25954	51	13	of	of	ADP
ajst-25954	51	14	existing	exist	VERB
ajst-25954	51	15	prediction	prediction	NOUN
ajst-25954	51	16	methods	method	NOUN
ajst-25954	51	17	,	,	PUNCT
ajst-25954	51	18	solve	solve	VERB
ajst-25954	51	19	the	the	DET
ajst-25954	51	20	lack	lack	NOUN
ajst-25954	51	21	of	of	ADP
ajst-25954	51	22	feature	feature	NOUN
ajst-25954	51	23	information	information	NOUN
ajst-25954	51	24	guided	guide	VERB
ajst-25954	51	25	by	by	ADP
ajst-25954	51	26	spatial	spatial	ADJ
ajst-25954	51	27	positions	position	NOUN
ajst-25954	51	28	of	of	ADP
ajst-25954	51	29	multichannel	multichannel	ADJ
ajst-25954	51	30	brain	brain	NOUN
ajst-25954	51	31	areas	area	NOUN
ajst-25954	51	32	,	,	PUNCT
ajst-25954	51	33	the	the	DET
ajst-25954	51	34	lack	lack	NOUN
ajst-25954	51	35	of	of	ADP
ajst-25954	51	36	modeling	model	VERB
ajst-25954	51	37	ability	ability	NOUN
ajst-25954	51	38	to	to	PART
ajst-25954	51	39	capture	capture	VERB
ajst-25954	51	40	interaction	interaction	NOUN
ajst-25954	51	41	features	feature	NOUN
ajst-25954	51	42	between	between	ADP
ajst-25954	51	43	data	datum	NOUN
ajst-25954	51	44	,	,	PUNCT
ajst-25954	51	45	and	and	CCONJ
ajst-25954	51	46	the	the	DET
ajst-25954	51	47	limitations	limitation	NOUN
ajst-25954	51	48	of	of	ADP
ajst-25954	51	49	model	model	NOUN
ajst-25954	51	50	performance	performance	NOUN
ajst-25954	51	51	caused	cause	VERB
ajst-25954	51	52	by	by	ADP
ajst-25954	51	53	highly	highly	ADV
ajst-25954	51	54	redundant	redundant	ADJ
ajst-25954	51	55	features	feature	NOUN
ajst-25954	51	56	.	.	PUNCT
ajst-25954	52	1	therefore	therefore	ADV
ajst-25954	52	2	,	,	PUNCT
ajst-25954	52	3	a	a	DET
ajst-25954	52	4	parallel	parallel	ADJ
ajst-25954	52	5	channel	channel	NOUN
ajst-25954	52	6	feature	feature	NOUN
ajst-25954	52	7	weighted	weight	VERB
ajst-25954	52	8	seizure	seizure	NOUN
ajst-25954	52	9	prediction	prediction	NOUN
ajst-25954	52	10	based	base	VERB
ajst-25954	52	11	on	on	ADP
ajst-25954	52	12	multi	multi	ADJ
ajst-25954	52	13	-	-	ADJ
ajst-25954	52	14	scale	scale	ADJ
ajst-25954	52	15	spatial	spatial	ADJ
ajst-25954	52	16	and	and	CCONJ
ajst-25954	52	17	temporal	temporal	ADJ
ajst-25954	52	18	factorization	factorization	NOUN
ajst-25954	52	19	machine	machine	NOUN
ajst-25954	52	20	(	(	PUNCT
ajst-25954	52	21	ms	ms	PROPN
ajst-25954	52	22	-	-	PUNCT
ajst-25954	52	23	stfm	stfm	NOUN
ajst-25954	52	24	-	-	PUNCT
ajst-25954	52	25	pcfwnet	pcfwnet	NOUN
ajst-25954	52	26	)	)	PUNCT
ajst-25954	52	27	is	be	AUX
ajst-25954	52	28	proposed	propose	VERB
ajst-25954	52	29	for	for	ADP
ajst-25954	52	30	seizure	seizure	NOUN
ajst-25954	52	31	prediction	prediction	NOUN
ajst-25954	52	32	.	.	PUNCT
ajst-25954	53	1	firstly	firstly	ADV
ajst-25954	53	2	,	,	PUNCT
ajst-25954	53	3	in	in	ADP
ajst-25954	53	4	order	order	NOUN
ajst-25954	53	5	to	to	PART
ajst-25954	53	6	fully	fully	ADV
ajst-25954	53	7	learn	learn	VERB
ajst-25954	53	8	the	the	DET
ajst-25954	53	9	correlation	correlation	NOUN
ajst-25954	53	10	between	between	ADP
ajst-25954	53	11	channels	channel	NOUN
ajst-25954	53	12	,	,	PUNCT
ajst-25954	53	13	3d	3d	NUM
ajst-25954	53	14	feature	feature	NOUN
ajst-25954	53	15	matrix	matrix	NOUN
ajst-25954	53	16	is	be	AUX
ajst-25954	53	17	employed	employ	VERB
ajst-25954	53	18	to	to	PART
ajst-25954	53	19	extract	extract	VERB
ajst-25954	53	20	the	the	DET
ajst-25954	53	21	temporal	temporal	ADJ
ajst-25954	53	22	and	and	CCONJ
ajst-25954	53	23	spatial	spatial	ADJ
ajst-25954	53	24	information	information	NOUN
ajst-25954	53	25	from	from	ADP
ajst-25954	53	26	multiple	multiple	ADJ
ajst-25954	53	27	channels	channel	NOUN
ajst-25954	53	28	.	.	PUNCT
ajst-25954	54	1	secondly	secondly	ADV
ajst-25954	54	2	,	,	PUNCT
ajst-25954	54	3	factorization	factorization	NOUN
ajst-25954	54	4	machine	machine	NOUN
ajst-25954	54	5	(	(	PUNCT
ajst-25954	54	6	fm	fm	NOUN
ajst-25954	54	7	)	)	PUNCT
ajst-25954	54	8	is	be	AUX
ajst-25954	54	9	used	use	VERB
ajst-25954	54	10	for	for	ADP
ajst-25954	54	11	feature	feature	NOUN
ajst-25954	54	12	combination	combination	NOUN
ajst-25954	54	13	and	and	CCONJ
ajst-25954	54	14	interaction	interaction	NOUN
ajst-25954	54	15	modeling	modeling	NOUN
ajst-25954	54	16	on	on	ADP
ajst-25954	54	17	the	the	DET
ajst-25954	54	18	output	output	NOUN
ajst-25954	54	19	of	of	ADP
ajst-25954	54	20	the	the	DET
ajst-25954	54	21	multi	multi	ADJ
ajst-25954	54	22	-	-	ADJ
ajst-25954	54	23	scale	scale	ADJ
ajst-25954	54	24	inception	inception	NOUN
ajst-25954	54	25	module	module	NOUN
ajst-25954	54	26	.	.	PUNCT
ajst-25954	55	1	finally	finally	ADV
ajst-25954	55	2	,	,	PUNCT
ajst-25954	55	3	a	a	DET
ajst-25954	55	4	parallel	parallel	ADJ
ajst-25954	55	5	channel	channel	NOUN
ajst-25954	55	6	feature	feature	NOUN
ajst-25954	55	7	weighted	weight	VERB
ajst-25954	55	8	network	network	NOUN
ajst-25954	55	9	(	(	PUNCT
ajst-25954	55	10	pcfwnet	pcfwnet	NOUN
ajst-25954	55	11	)	)	PUNCT
ajst-25954	55	12	is	be	AUX
ajst-25954	55	13	proposed	propose	VERB
ajst-25954	55	14	to	to	PART
ajst-25954	55	15	effectively	effectively	ADV
ajst-25954	55	16	learn	learn	VERB
ajst-25954	55	17	multi	multi	ADJ
ajst-25954	55	18	-	-	ADJ
ajst-25954	55	19	domain	domain	ADJ
ajst-25954	55	20	features	feature	NOUN
ajst-25954	55	21	,	,	PUNCT
ajst-25954	55	22	which	which	PRON
ajst-25954	55	23	introduces	introduce	VERB
ajst-25954	55	24	an	an	DET
ajst-25954	55	25	efficient	efficient	ADJ
ajst-25954	55	26	channel	channel	NOUN
ajst-25954	55	27	attention	attention	NOUN
ajst-25954	55	28	mechanism	mechanism	NOUN
ajst-25954	55	29	(	(	PUNCT
ajst-25954	55	30	eca	eca	NOUN
ajst-25954	55	31	)	)	PUNCT
ajst-25954	55	32	to	to	PART
ajst-25954	55	33	reflect	reflect	VERB
ajst-25954	55	34	discriminant	discriminant	ADJ
ajst-25954	55	35	representations	representation	NOUN
ajst-25954	55	36	of	of	ADP
ajst-25954	55	37	seizure	seizure	NOUN
ajst-25954	55	38	prediction	prediction	NOUN
ajst-25954	55	39	.	.	PUNCT
ajst-25954	56	1	the	the	DET
ajst-25954	56	2	proposed	propose	VERB
ajst-25954	56	3	ms	ms	PROPN
ajst-25954	56	4	-	-	PUNCT
ajst-25954	56	5	stfm	stfm	NOUN
ajst-25954	56	6	-	-	PUNCT
ajst-25954	56	7	pcfwnet	pcfwnet	NOUN
ajst-25954	56	8	is	be	AUX
ajst-25954	56	9	evaluated	evaluate	VERB
ajst-25954	56	10	on	on	ADP
ajst-25954	56	11	the	the	DET
ajst-25954	56	12	chbmit	chbmit	NOUN
ajst-25954	56	13	and	and	CCONJ
ajst-25954	56	14	bonn	bonn	PROPN
ajst-25954	56	15	datasets	dataset	NOUN
ajst-25954	56	16	.	.	PUNCT
ajst-25954	57	1	the	the	DET
ajst-25954	57	2	proposed	propose	VERB
ajst-25954	57	3	method	method	NOUN
ajst-25954	57	4	achieves	achieve	VERB
ajst-25954	57	5	the	the	DET
ajst-25954	57	6	encouraging	encouraging	ADJ
ajst-25954	57	7	performance	performance	NOUN
ajst-25954	57	8	results	result	NOUN
ajst-25954	57	9	compared	compare	VERB
ajst-25954	57	10	to	to	ADP
ajst-25954	57	11	the	the	DET
ajst-25954	57	12	most	most	ADV
ajst-25954	57	13	advanced	advanced	ADJ
ajst-25954	57	14	techniques	technique	NOUN
ajst-25954	57	15	,	,	PUNCT
ajst-25954	57	16	which	which	PRON
ajst-25954	57	17	validated	validate	VERB
ajst-25954	57	18	the	the	DET
ajst-25954	57	19	breakthrough	breakthrough	NOUN
ajst-25954	57	20	of	of	ADP
ajst-25954	57	21	the	the	DET
ajst-25954	57	22	83	83	NUM
ajst-25954	57	23	method	method	NOUN
ajst-25954	57	24	in	in	ADP
ajst-25954	57	25	the	the	DET
ajst-25954	57	26	task	task	NOUN
ajst-25954	57	27	of	of	ADP
ajst-25954	57	28	seizure	seizure	NOUN
ajst-25954	57	29	prediction	prediction	NOUN
ajst-25954	57	30	.	.	PUNCT
ajst-25954	58	1	in	in	ADP
ajst-25954	58	2	general	general	ADJ
ajst-25954	58	3	,	,	PUNCT
ajst-25954	58	4	the	the	DET
ajst-25954	58	5	main	main	ADJ
ajst-25954	58	6	contributions	contribution	NOUN
ajst-25954	58	7	of	of	ADP
ajst-25954	58	8	this	this	DET
ajst-25954	58	9	paper	paper	NOUN
ajst-25954	58	10	are	be	AUX
ajst-25954	58	11	summarized	summarize	VERB
ajst-25954	58	12	as	as	SCONJ
ajst-25954	58	13	follows	follow	VERB
ajst-25954	58	14	:	:	PUNCT
ajst-25954	58	15	(	(	PUNCT
ajst-25954	58	16	1	1	X
ajst-25954	58	17	)	)	PUNCT
ajst-25954	58	18	a	a	DET
ajst-25954	58	19	ms	ms	PROPN
ajst-25954	58	20	-	-	PUNCT
ajst-25954	58	21	stfm	stfm	NOUN
ajst-25954	58	22	-	-	PUNCT
ajst-25954	58	23	pcfwnet	pcfwnet	NOUN
ajst-25954	58	24	is	be	AUX
ajst-25954	58	25	proposed	propose	VERB
ajst-25954	58	26	to	to	PART
ajst-25954	58	27	predict	predict	VERB
ajst-25954	58	28	seizures	seizure	NOUN
ajst-25954	58	29	,	,	PUNCT
ajst-25954	58	30	which	which	PRON
ajst-25954	58	31	can	can	AUX
ajst-25954	58	32	effectively	effectively	ADV
ajst-25954	58	33	extract	extract	VERB
ajst-25954	58	34	spatiotemporal	spatiotemporal	ADJ
ajst-25954	58	35	features	feature	NOUN
ajst-25954	58	36	and	and	CCONJ
ajst-25954	58	37	obtain	obtain	VERB
ajst-25954	58	38	good	good	ADJ
ajst-25954	58	39	prediction	prediction	NOUN
ajst-25954	58	40	performance	performance	NOUN
ajst-25954	58	41	on	on	ADP
ajst-25954	58	42	the	the	DET
ajst-25954	58	43	chb	chb	NOUN
ajst-25954	58	44	-	-	PUNCT
ajst-25954	58	45	mit	mit	NOUN
ajst-25954	58	46	and	and	CCONJ
ajst-25954	58	47	bonn	bonn	PROPN
ajst-25954	58	48	datasets	dataset	NOUN
ajst-25954	58	49	,	,	PUNCT
ajst-25954	58	50	respectively	respectively	ADV
ajst-25954	58	51	.	.	PUNCT
ajst-25954	59	1	(	(	PUNCT
ajst-25954	59	2	2	2	X
ajst-25954	59	3	)	)	PUNCT
ajst-25954	59	4	a	a	DET
ajst-25954	59	5	3d	3d	NUM
ajst-25954	59	6	feature	feature	NOUN
ajst-25954	59	7	matrix	matrix	NOUN
ajst-25954	59	8	is	be	AUX
ajst-25954	59	9	introduced	introduce	VERB
ajst-25954	59	10	to	to	PART
ajst-25954	59	11	extract	extract	VERB
ajst-25954	59	12	eeg	eeg	NOUN
ajst-25954	59	13	features	feature	NOUN
ajst-25954	59	14	in	in	ADP
ajst-25954	59	15	time	time	NOUN
ajst-25954	59	16	domain	domain	NOUN
ajst-25954	59	17	and	and	CCONJ
ajst-25954	59	18	spatial	spatial	ADJ
ajst-25954	59	19	domain	domain	NOUN
ajst-25954	59	20	,	,	PUNCT
ajst-25954	59	21	which	which	PRON
ajst-25954	59	22	captures	capture	VERB
ajst-25954	59	23	the	the	DET
ajst-25954	59	24	correlation	correlation	NOUN
ajst-25954	59	25	information	information	NOUN
ajst-25954	59	26	between	between	ADP
ajst-25954	59	27	channels	channel	NOUN
ajst-25954	59	28	,	,	PUNCT
ajst-25954	59	29	it	it	PRON
ajst-25954	59	30	makes	make	VERB
ajst-25954	59	31	up	up	ADP
ajst-25954	59	32	for	for	ADP
ajst-25954	59	33	the	the	DET
ajst-25954	59	34	deficiency	deficiency	NOUN
ajst-25954	59	35	of	of	ADP
ajst-25954	59	36	extracting	extract	VERB
ajst-25954	59	37	spatiotemporal	spatiotemporal	ADJ
ajst-25954	59	38	feature	feature	NOUN
ajst-25954	59	39	representations	representation	NOUN
ajst-25954	59	40	in	in	ADP
ajst-25954	59	41	the	the	DET
ajst-25954	59	42	past	past	NOUN
ajst-25954	59	43	.	.	PUNCT
ajst-25954	60	1	(	(	PUNCT
ajst-25954	60	2	3	3	X
ajst-25954	60	3	)	)	PUNCT
ajst-25954	60	4	the	the	DET
ajst-25954	60	5	fm	fm	PROPN
ajst-25954	60	6	hidden	hide	VERB
ajst-25954	60	7	layer	layer	NOUN
ajst-25954	60	8	is	be	AUX
ajst-25954	60	9	utilized	utilize	VERB
ajst-25954	60	10	to	to	PART
ajst-25954	60	11	conduct	conduct	VERB
ajst-25954	60	12	interactive	interactive	ADJ
ajst-25954	60	13	modeling	modeling	NOUN
ajst-25954	60	14	for	for	ADP
ajst-25954	60	15	the	the	DET
ajst-25954	60	16	output	output	NOUN
ajst-25954	60	17	generation	generation	NOUN
ajst-25954	60	18	of	of	ADP
ajst-25954	60	19	the	the	DET
ajst-25954	60	20	multi	multi	ADJ
ajst-25954	60	21	-	-	ADJ
ajst-25954	60	22	scale	scale	ADJ
ajst-25954	60	23	inception	inception	NOUN
ajst-25954	60	24	module	module	NOUN
ajst-25954	60	25	,	,	PUNCT
ajst-25954	60	26	so	so	SCONJ
ajst-25954	60	27	that	that	SCONJ
ajst-25954	60	28	the	the	DET
ajst-25954	60	29	gain	gain	NOUN
ajst-25954	60	30	information	information	NOUN
ajst-25954	60	31	for	for	ADP
ajst-25954	60	32	seizure	seizure	NOUN
ajst-25954	60	33	prediction	prediction	NOUN
ajst-25954	60	34	can	can	AUX
ajst-25954	60	35	be	be	AUX
ajst-25954	60	36	fully	fully	ADV
ajst-25954	60	37	captured	capture	VERB
ajst-25954	60	38	.	.	PUNCT
ajst-25954	61	1	(	(	PUNCT
ajst-25954	61	2	4	4	X
ajst-25954	61	3	)	)	PUNCT
ajst-25954	61	4	a	a	DET
ajst-25954	61	5	pcfwnet	pcfwnet	NOUN
ajst-25954	61	6	is	be	AUX
ajst-25954	61	7	proposed	propose	VERB
ajst-25954	61	8	,	,	PUNCT
ajst-25954	61	9	it	it	PRON
ajst-25954	61	10	can	can	AUX
ajst-25954	61	11	effectively	effectively	ADV
ajst-25954	61	12	adjust	adjust	VERB
ajst-25954	61	13	the	the	DET
ajst-25954	61	14	channel	channel	NOUN
ajst-25954	61	15	weights	weight	NOUN
ajst-25954	61	16	to	to	PART
ajst-25954	61	17	capture	capture	VERB
ajst-25954	61	18	the	the	DET
ajst-25954	61	19	global	global	ADJ
ajst-25954	61	20	spatiotemporal	spatiotemporal	ADJ
ajst-25954	61	21	features	feature	NOUN
ajst-25954	61	22	,	,	PUNCT
ajst-25954	61	23	and	and	CCONJ
ajst-25954	61	24	realize	realize	VERB
ajst-25954	61	25	the	the	DET
ajst-25954	61	26	discriminant	discriminant	ADJ
ajst-25954	61	27	representation	representation	NOUN
ajst-25954	61	28	of	of	ADP
ajst-25954	61	29	mapped	map	VERB
ajst-25954	61	30	seizure	seizure	NOUN
ajst-25954	61	31	prediction	prediction	NOUN
ajst-25954	61	32	and	and	CCONJ
ajst-25954	61	33	improve	improve	VERB
ajst-25954	61	34	the	the	DET
ajst-25954	61	35	prediction	prediction	NOUN
ajst-25954	61	36	performance	performance	NOUN
ajst-25954	61	37	.	.	PUNCT
ajst-25954	62	1	2	2	X
ajst-25954	62	2	.	.	X
ajst-25954	62	3	mathodology	mathodology	NOUN
ajst-25954	62	4	the	the	DET
ajst-25954	62	5	proposed	propose	VERB
ajst-25954	62	6	ms	ms	PROPN
ajst-25954	62	7	-	-	PUNCT
ajst-25954	62	8	stfm	stfm	NOUN
ajst-25954	62	9	-	-	PUNCT
ajst-25954	62	10	pcfwnet	pcfwnet	NOUN
ajst-25954	62	11	seizure	seizure	NOUN
ajst-25954	62	12	prediction	prediction	NOUN
ajst-25954	62	13	framework	framework	NOUN
ajst-25954	62	14	is	be	AUX
ajst-25954	62	15	shown	show	VERB
ajst-25954	62	16	in	in	ADP
ajst-25954	62	17	fig	fig	NOUN
ajst-25954	62	18	.	.	PUNCT
ajst-25954	63	1	1	1	X
ajst-25954	63	2	.	.	PUNCT
ajst-25954	63	3	the	the	DET
ajst-25954	63	4	overall	overall	ADJ
ajst-25954	63	5	architecture	architecture	NOUN
ajst-25954	63	6	is	be	AUX
ajst-25954	63	7	mainly	mainly	ADV
ajst-25954	63	8	composed	compose	VERB
ajst-25954	63	9	of	of	ADP
ajst-25954	63	10	multi	multi	ADJ
ajst-25954	63	11	-	-	ADJ
ajst-25954	63	12	scale	scale	ADJ
ajst-25954	63	13	inception	inception	NOUN
ajst-25954	63	14	module	module	NOUN
ajst-25954	63	15	,	,	PUNCT
ajst-25954	63	16	fm	fm	NOUN
ajst-25954	63	17	layer	layer	NOUN
ajst-25954	63	18	and	and	CCONJ
ajst-25954	63	19	pcfwnet	pcfwnet	NOUN
ajst-25954	63	20	,	,	PUNCT
ajst-25954	63	21	which	which	PRON
ajst-25954	63	22	can	can	AUX
ajst-25954	63	23	be	be	AUX
ajst-25954	63	24	summarized	summarize	VERB
ajst-25954	63	25	as	as	SCONJ
ajst-25954	63	26	follows	follow	VERB
ajst-25954	63	27	:	:	PUNCT
ajst-25954	63	28	(	(	PUNCT
ajst-25954	63	29	1	1	X
ajst-25954	63	30	)	)	PUNCT
ajst-25954	63	31	3d	3d	NOUN
ajst-25954	63	32	feature	feature	NOUN
ajst-25954	63	33	matrix	matrix	NOUN
ajst-25954	63	34	is	be	AUX
ajst-25954	63	35	mainly	mainly	ADV
ajst-25954	63	36	used	use	VERB
ajst-25954	63	37	to	to	PART
ajst-25954	63	38	extract	extract	VERB
ajst-25954	63	39	multichannel	multichannel	ADJ
ajst-25954	63	40	spatiotemporal	spatiotemporal	ADJ
ajst-25954	63	41	features	feature	NOUN
ajst-25954	63	42	from	from	ADP
ajst-25954	63	43	eeg	eeg	NOUN
ajst-25954	63	44	signals	signal	NOUN
ajst-25954	63	45	,	,	PUNCT
ajst-25954	63	46	which	which	PRON
ajst-25954	63	47	the	the	DET
ajst-25954	63	48	features	feature	NOUN
ajst-25954	63	49	are	be	AUX
ajst-25954	63	50	extracted	extract	VERB
ajst-25954	63	51	through	through	ADP
ajst-25954	63	52	1×1	1×1	NUM
ajst-25954	63	53	convolution	convolution	NOUN
ajst-25954	63	54	kernel	kernel	NOUN
ajst-25954	63	55	.	.	PUNCT
ajst-25954	64	1	the	the	DET
ajst-25954	64	2	inception	inception	NOUN
ajst-25954	64	3	module	module	NOUN
ajst-25954	64	4	of	of	ADP
ajst-25954	64	5	different	different	ADJ
ajst-25954	64	6	scales	scale	NOUN
ajst-25954	64	7	is	be	AUX
ajst-25954	64	8	employed	employ	VERB
ajst-25954	64	9	to	to	PART
ajst-25954	64	10	convolve	convolve	VERB
ajst-25954	64	11	the	the	DET
ajst-25954	64	12	feature	feature	NOUN
ajst-25954	64	13	graphs	graph	NOUN
ajst-25954	64	14	,	,	PUNCT
ajst-25954	64	15	and	and	CCONJ
ajst-25954	64	16	then	then	ADV
ajst-25954	64	17	merged	merge	VERB
ajst-25954	64	18	in	in	ADP
ajst-25954	64	19	the	the	DET
ajst-25954	64	20	same	same	ADJ
ajst-25954	64	21	dimension	dimension	NOUN
ajst-25954	64	22	.	.	PUNCT
ajst-25954	65	1	the	the	DET
ajst-25954	65	2	gain	gain	NOUN
ajst-25954	65	3	information	information	NOUN
ajst-25954	65	4	related	relate	VERB
ajst-25954	65	5	to	to	PART
ajst-25954	65	6	seizure	seizure	VERB
ajst-25954	65	7	state	state	NOUN
ajst-25954	65	8	in	in	ADP
ajst-25954	65	9	adjacent	adjacent	ADJ
ajst-25954	65	10	electrodes	electrode	NOUN
ajst-25954	65	11	is	be	AUX
ajst-25954	65	12	extracted	extract	VERB
ajst-25954	65	13	by	by	ADP
ajst-25954	65	14	convolution	convolution	NOUN
ajst-25954	65	15	operation	operation	NOUN
ajst-25954	65	16	of	of	ADP
ajst-25954	65	17	5×5	5×5	NUM
ajst-25954	65	18	convolution	convolution	NOUN
ajst-25954	65	19	kernel	kernel	NOUN
ajst-25954	65	20	.	.	PUNCT
ajst-25954	66	1	(	(	PUNCT
ajst-25954	66	2	2	2	X
ajst-25954	66	3	)	)	PUNCT
ajst-25954	66	4	the	the	DET
ajst-25954	66	5	fm	fm	PROPN
ajst-25954	66	6	layer	layer	NOUN
ajst-25954	66	7	is	be	AUX
ajst-25954	66	8	introduced	introduce	VERB
ajst-25954	66	9	to	to	PART
ajst-25954	66	10	conduct	conduct	VERB
ajst-25954	66	11	the	the	DET
ajst-25954	66	12	feature	feature	NOUN
ajst-25954	66	13	interaction	interaction	NOUN
ajst-25954	66	14	modeling	modeling	NOUN
ajst-25954	66	15	on	on	ADP
ajst-25954	66	16	the	the	DET
ajst-25954	66	17	merged	merge	VERB
ajst-25954	66	18	outputs	output	NOUN
ajst-25954	66	19	of	of	ADP
ajst-25954	66	20	inception	inception	ADJ
ajst-25954	66	21	modules	module	NOUN
ajst-25954	66	22	with	with	ADP
ajst-25954	66	23	three	three	NUM
ajst-25954	66	24	different	different	ADJ
ajst-25954	66	25	scales	scale	NOUN
ajst-25954	66	26	,	,	PUNCT
ajst-25954	66	27	the	the	DET
ajst-25954	66	28	interaction	interaction	NOUN
ajst-25954	66	29	and	and	CCONJ
ajst-25954	66	30	correlation	correlation	NOUN
ajst-25954	66	31	information	information	NOUN
ajst-25954	66	32	between	between	ADP
ajst-25954	66	33	channel	channel	NOUN
ajst-25954	66	34	features	feature	NOUN
ajst-25954	66	35	is	be	AUX
ajst-25954	66	36	captured	capture	VERB
ajst-25954	66	37	.	.	PUNCT
ajst-25954	67	1	(	(	PUNCT
ajst-25954	67	2	3	3	X
ajst-25954	67	3	)	)	PUNCT
ajst-25954	67	4	the	the	DET
ajst-25954	67	5	pcfwnet	pcfwnet	NOUN
ajst-25954	67	6	can	can	AUX
ajst-25954	67	7	dynamically	dynamically	ADV
ajst-25954	67	8	adjust	adjust	VERB
ajst-25954	67	9	channel	channel	NOUN
ajst-25954	67	10	weights	weight	NOUN
ajst-25954	67	11	to	to	PART
ajst-25954	67	12	capture	capture	VERB
ajst-25954	67	13	global	global	ADJ
ajst-25954	67	14	spatiotemporal	spatiotemporal	ADJ
ajst-25954	67	15	features	feature	NOUN
ajst-25954	67	16	between	between	ADP
ajst-25954	67	17	fm	fm	PROPN
ajst-25954	67	18	and	and	CCONJ
ajst-25954	67	19	linear	linear	VERB
ajst-25954	67	20	hidden	hide	VERB
ajst-25954	67	21	layer	layer	NOUN
ajst-25954	67	22	outputs	output	NOUN
ajst-25954	67	23	.	.	PUNCT
ajst-25954	68	1	the	the	DET
ajst-25954	68	2	detailed	detailed	ADJ
ajst-25954	68	3	steps	step	NOUN
ajst-25954	68	4	are	be	AUX
ajst-25954	68	5	as	as	SCONJ
ajst-25954	68	6	follows	follow	VERB
ajst-25954	68	7	.	.	PUNCT
ajst-25954	69	1	1	1	X
ajst-25954	69	2	.	.	X
ajst-25954	69	3	multi	multi	ADJ
ajst-25954	69	4	-	-	ADJ
ajst-25954	69	5	scale	scale	ADJ
ajst-25954	69	6	feature	feature	NOUN
ajst-25954	69	7	merging	merge	VERB
ajst-25954	69	8	convolutional	convolutional	ADJ
ajst-25954	69	9	network	network	NOUN
ajst-25954	69	10	reference	reference	NOUN
ajst-25954	69	11	[	[	X
ajst-25954	69	12	24	24	NUM
ajst-25954	69	13	]	]	PUNCT
ajst-25954	69	14	proposed	propose	VERB
ajst-25954	69	15	the	the	DET
ajst-25954	69	16	planar	planar	ADJ
ajst-25954	69	17	graph	graph	NOUN
ajst-25954	69	18	and	and	CCONJ
ajst-25954	69	19	mapping	mapping	NOUN
ajst-25954	69	20	matrix	matrix	NOUN
ajst-25954	69	21	of	of	ADP
ajst-25954	69	22	the	the	DET
ajst-25954	69	23	international	international	ADJ
ajst-25954	69	24	10/20	10/20	NUM
ajst-25954	69	25	system	system	NOUN
ajst-25954	69	26	,	,	PUNCT
ajst-25954	69	27	and	and	CCONJ
ajst-25954	69	28	constructed	construct	VERB
ajst-25954	69	29	the	the	DET
ajst-25954	69	30	feature	feature	NOUN
ajst-25954	69	31	matrix	matrix	NOUN
ajst-25954	69	32	by	by	ADP
ajst-25954	69	33	using	use	VERB
ajst-25954	69	34	the	the	DET
ajst-25954	69	35	global	global	ADJ
ajst-25954	69	36	information	information	NOUN
ajst-25954	69	37	and	and	CCONJ
ajst-25954	69	38	spatial	spatial	ADJ
ajst-25954	69	39	features	feature	NOUN
ajst-25954	69	40	of	of	ADP
ajst-25954	69	41	electrode	electrode	NOUN
ajst-25954	69	42	positions	position	NOUN
ajst-25954	69	43	.	.	PUNCT
ajst-25954	70	1	according	accord	VERB
ajst-25954	70	2	to	to	ADP
ajst-25954	70	3	the	the	DET
ajst-25954	70	4	relative	relative	ADJ
ajst-25954	70	5	position	position	NOUN
ajst-25954	70	6	of	of	ADP
ajst-25954	70	7	the	the	DET
ajst-25954	70	8	electrodes	electrode	NOUN
ajst-25954	70	9	,	,	PUNCT
ajst-25954	70	10	the	the	DET
ajst-25954	70	11	time	time	NOUN
ajst-25954	70	12	domain	domain	NOUN
ajst-25954	70	13	features	feature	NOUN
ajst-25954	70	14	of	of	ADP
ajst-25954	70	15	the	the	DET
ajst-25954	70	16	multi	multi	ADJ
ajst-25954	70	17	-	-	ADJ
ajst-25954	70	18	channel	channel	NOUN
ajst-25954	70	19	are	be	AUX
ajst-25954	70	20	filled	fill	VERB
ajst-25954	70	21	into	into	ADP
ajst-25954	70	22	the	the	DET
ajst-25954	70	23	corresponding	corresponding	ADJ
ajst-25954	70	24	position	position	NOUN
ajst-25954	70	25	of	of	ADP
ajst-25954	70	26	the	the	DET
ajst-25954	70	27	feature	feature	NOUN
ajst-25954	70	28	matrix	matrix	NOUN
ajst-25954	70	29	according	accord	VERB
ajst-25954	70	30	to	to	ADP
ajst-25954	70	31	the	the	DET
ajst-25954	70	32	coordinates	coordinate	NOUN
ajst-25954	70	33	.	.	PUNCT
ajst-25954	71	1	to	to	PART
ajst-25954	71	2	ensure	ensure	VERB
ajst-25954	71	3	the	the	DET
ajst-25954	71	4	integrity	integrity	NOUN
ajst-25954	71	5	of	of	ADP
ajst-25954	71	6	the	the	DET
ajst-25954	71	7	feature	feature	NOUN
ajst-25954	71	8	matrix	matrix	NOUN
ajst-25954	71	9	,	,	PUNCT
ajst-25954	71	10	the	the	DET
ajst-25954	71	11	number	number	NOUN
ajst-25954	71	12	"	"	PUNCT
ajst-25954	71	13	0	0	NUM
ajst-25954	71	14	"	"	PUNCT
ajst-25954	71	15	is	be	AUX
ajst-25954	71	16	used	use	VERB
ajst-25954	71	17	to	to	PART
ajst-25954	71	18	represent	represent	VERB
ajst-25954	71	19	the	the	DET
ajst-25954	71	20	unused	unused	ADJ
ajst-25954	71	21	channels	channel	NOUN
ajst-25954	71	22	,	,	PUNCT
ajst-25954	71	23	as	as	SCONJ
ajst-25954	71	24	shown	show	VERB
ajst-25954	71	25	in	in	ADP
ajst-25954	71	26	fig	fig	NOUN
ajst-25954	71	27	.	.	PUNCT
ajst-25954	72	1	1	1	NUM
ajst-25954	72	2	.	.	PUNCT
ajst-25954	72	3	according	accord	VERB
ajst-25954	72	4	to	to	ADP
ajst-25954	72	5	literature	literature	NOUN
ajst-25954	72	6	[	[	X
ajst-25954	72	7	24	24	NUM
ajst-25954	72	8	]	]	PUNCT
ajst-25954	72	9	,	,	PUNCT
ajst-25954	72	10	a	a	DET
ajst-25954	72	11	3d	3d	NUM
ajst-25954	72	12	feature	feature	NOUN
ajst-25954	72	13	matrix	matrix	NOUN
ajst-25954	72	14	of	of	ADP
ajst-25954	72	15	9×9×1	9×9×1	NUM
ajst-25954	72	16	is	be	AUX
ajst-25954	72	17	constructed	construct	VERB
ajst-25954	72	18	for	for	ADP
ajst-25954	72	19	each	each	DET
ajst-25954	72	20	sample	sample	NOUN
ajst-25954	72	21	according	accord	VERB
ajst-25954	72	22	to	to	ADP
ajst-25954	72	23	the	the	DET
ajst-25954	72	24	rules	rule	NOUN
ajst-25954	72	25	.	.	PUNCT
ajst-25954	73	1	the	the	DET
ajst-25954	73	2	time	time	NOUN
ajst-25954	73	3	-	-	PUNCT
ajst-25954	73	4	domain	domain	NOUN
ajst-25954	73	5	features	feature	NOUN
ajst-25954	73	6	extracted	extract	VERB
ajst-25954	73	7	in	in	ADP
ajst-25954	73	8	this	this	DET
ajst-25954	73	9	paper	paper	NOUN
ajst-25954	73	10	include	include	VERB
ajst-25954	73	11	mean	mean	NOUN
ajst-25954	73	12	value	value	NOUN
ajst-25954	73	13	,	,	PUNCT
ajst-25954	73	14	variance	variance	NOUN
ajst-25954	73	15	,	,	PUNCT
ajst-25954	73	16	standard	standard	ADJ
ajst-25954	73	17	value	value	NOUN
ajst-25954	73	18	,	,	PUNCT
ajst-25954	73	19	fuzzy	fuzzy	ADJ
ajst-25954	73	20	entropy	entropy	NOUN
ajst-25954	73	21	,	,	PUNCT
ajst-25954	73	22	skewness	skewness	NOUN
ajst-25954	73	23	and	and	CCONJ
ajst-25954	73	24	peak	peak	NOUN
ajst-25954	73	25	value	value	NOUN
ajst-25954	73	26	,	,	PUNCT
ajst-25954	73	27	which	which	PRON
ajst-25954	73	28	these	these	DET
ajst-25954	73	29	6	6	NUM
ajst-25954	73	30	time	time	NOUN
ajst-25954	73	31	-	-	PUNCT
ajst-25954	73	32	domain	domain	NOUN
ajst-25954	73	33	features	feature	NOUN
ajst-25954	73	34	are	be	AUX
ajst-25954	73	35	constructed	construct	VERB
ajst-25954	73	36	as	as	ADP
ajst-25954	73	37	2d	2d	NUM
ajst-25954	73	38	feature	feature	NOUN
ajst-25954	73	39	matrix	matrix	NOUN
ajst-25954	73	40	and	and	CCONJ
ajst-25954	73	41	superimposed	superimpose	VERB
ajst-25954	73	42	into	into	ADP
ajst-25954	73	43	the	the	DET
ajst-25954	73	44	3d	3d	NUM
ajst-25954	73	45	feature	feature	NOUN
ajst-25954	73	46	matrix	matrix	NOUN
ajst-25954	73	47	9×9×6	9×9×6	NUM
ajst-25954	73	48	on	on	ADP
ajst-25954	73	49	the	the	DET
ajst-25954	73	50	dimension	dimension	NOUN
ajst-25954	73	51	9×9×1	9×9×1	NOUN
ajst-25954	73	52	.	.	PUNCT
ajst-25954	74	1	the	the	DET
ajst-25954	74	2	matrix	matrix	NOUN
ajst-25954	74	3	contains	contain	VERB
ajst-25954	74	4	not	not	PART
ajst-25954	74	5	only	only	ADV
ajst-25954	74	6	the	the	DET
ajst-25954	74	7	timing	timing	NOUN
ajst-25954	74	8	features	feature	NOUN
ajst-25954	74	9	within	within	ADP
ajst-25954	74	10	the	the	DET
ajst-25954	74	11	eeg	eeg	NOUN
ajst-25954	74	12	channels	channel	NOUN
ajst-25954	74	13	,	,	PUNCT
ajst-25954	74	14	but	but	CCONJ
ajst-25954	74	15	also	also	ADV
ajst-25954	74	16	the	the	DET
ajst-25954	74	17	position	position	NOUN
ajst-25954	74	18	correlation	correlation	NOUN
ajst-25954	74	19	information	information	NOUN
ajst-25954	74	20	of	of	ADP
ajst-25954	74	21	the	the	DET
ajst-25954	74	22	electrode	electrode	NOUN
ajst-25954	74	23	channels	channel	NOUN
ajst-25954	74	24	.	.	PUNCT
ajst-25954	75	1	the	the	DET
ajst-25954	75	2	extracted	extract	VERB
ajst-25954	75	3	spatiotemporal	spatiotemporal	ADJ
ajst-25954	75	4	features	feature	NOUN
ajst-25954	75	5	are	be	AUX
ajst-25954	75	6	input	input	VERB
ajst-25954	75	7	into	into	ADP
ajst-25954	75	8	the	the	DET
ajst-25954	75	9	multi	multi	ADJ
ajst-25954	75	10	-	-	ADJ
ajst-25954	75	11	scale	scale	ADJ
ajst-25954	75	12	feature	feature	NOUN
ajst-25954	75	13	merging	merge	VERB
ajst-25954	75	14	convolutional	convolutional	ADJ
ajst-25954	75	15	network	network	NOUN
ajst-25954	75	16	,	,	PUNCT
ajst-25954	75	17	as	as	SCONJ
ajst-25954	75	18	shown	show	VERB
ajst-25954	75	19	in	in	ADP
ajst-25954	75	20	fig	fig	NOUN
ajst-25954	75	21	.	.	PUNCT
ajst-25954	76	1	2	2	X
ajst-25954	76	2	.	.	X
ajst-25954	76	3	firstly	firstly	ADV
ajst-25954	76	4	,	,	PUNCT
ajst-25954	76	5	the	the	DET
ajst-25954	76	6	eeg	eeg	NOUN
ajst-25954	76	7	features	feature	NOUN
ajst-25954	76	8	of	of	ADP
ajst-25954	76	9	each	each	DET
ajst-25954	76	10	channel	channel	NOUN
ajst-25954	76	11	are	be	AUX
ajst-25954	76	12	obtained	obtain	VERB
ajst-25954	76	13	by	by	ADP
ajst-25954	76	14	1×1	1×1	NUM
ajst-25954	76	15	convolution	convolution	NOUN
ajst-25954	76	16	kernel	kernel	NOUN
ajst-25954	76	17	according	accord	VERB
ajst-25954	76	18	to	to	ADP
ajst-25954	76	19	the	the	DET
ajst-25954	76	20	input	input	NOUN
ajst-25954	76	21	3d	3d	NUM
ajst-25954	76	22	feature	feature	NOUN
ajst-25954	76	23	matrix	matrix	NOUN
ajst-25954	76	24	.	.	PUNCT
ajst-25954	77	1	the	the	DET
ajst-25954	77	2	extracted	extract	VERB
ajst-25954	77	3	features	feature	NOUN
ajst-25954	77	4	are	be	AUX
ajst-25954	77	5	input	input	VERB
ajst-25954	77	6	into	into	ADP
ajst-25954	77	7	convolution	convolution	NOUN
ajst-25954	77	8	kernels	kernel	NOUN
ajst-25954	77	9	of	of	ADP
ajst-25954	77	10	different	different	ADJ
ajst-25954	77	11	scales	scale	NOUN
ajst-25954	77	12	,	,	PUNCT
ajst-25954	77	13	the	the	DET
ajst-25954	77	14	convolution	convolution	NOUN
ajst-25954	77	15	kernel	kernel	NOUN
ajst-25954	77	16	sizes	size	NOUN
ajst-25954	77	17	are	be	AUX
ajst-25954	77	18	3×3	3×3	NUM
ajst-25954	77	19	,	,	PUNCT
ajst-25954	77	20	5×5	5×5	NUM
ajst-25954	77	21	,	,	PUNCT
ajst-25954	77	22	and	and	CCONJ
ajst-25954	77	23	7×7	7×7	NOUN
ajst-25954	77	24	respectively	respectively	ADV
ajst-25954	77	25	.	.	PUNCT
ajst-25954	78	1	the	the	DET
ajst-25954	78	2	features	feature	NOUN
ajst-25954	78	3	extracted	extract	VERB
ajst-25954	78	4	by	by	ADP
ajst-25954	78	5	multi	multi	ADJ
ajst-25954	78	6	-	-	ADJ
ajst-25954	78	7	scale	scale	ADJ
ajst-25954	78	8	convolution	convolution	NOUN
ajst-25954	78	9	are	be	AUX
ajst-25954	78	10	used	use	VERB
ajst-25954	78	11	for	for	ADP
ajst-25954	78	12	convolution	convolution	NOUN
ajst-25954	78	13	operation	operation	NOUN
ajst-25954	78	14	,	,	PUNCT
ajst-25954	78	15	so	so	CCONJ
ajst-25954	78	16	the	the	DET
ajst-25954	78	17	feature	feature	NOUN
ajst-25954	78	18	representations	representation	NOUN
ajst-25954	78	19	at	at	ADP
ajst-25954	78	20	different	different	ADJ
ajst-25954	78	21	scales	scale	NOUN
ajst-25954	78	22	can	can	AUX
ajst-25954	78	23	be	be	AUX
ajst-25954	78	24	obtained.so	obtained.so	VERB
ajst-25954	78	25	the	the	DET
ajst-25954	78	26	feature	feature	NOUN
ajst-25954	78	27	representations	representation	NOUN
ajst-25954	78	28	at	at	ADP
ajst-25954	78	29	different	different	ADJ
ajst-25954	78	30	scales	scale	NOUN
ajst-25954	78	31	can	can	AUX
ajst-25954	78	32	be	be	AUX
ajst-25954	78	33	obtained	obtain	VERB
ajst-25954	78	34	.	.	PUNCT
ajst-25954	79	1	then	then	ADV
ajst-25954	79	2	,	,	PUNCT
ajst-25954	79	3	the	the	DET
ajst-25954	79	4	multi	multi	ADJ
ajst-25954	79	5	-	-	ADJ
ajst-25954	79	6	scale	scale	ADJ
ajst-25954	79	7	convolution	convolution	NOUN
ajst-25954	79	8	kernel	kernel	NOUN
ajst-25954	79	9	is	be	AUX
ajst-25954	79	10	merged	merge	VERB
ajst-25954	79	11	on	on	ADP
ajst-25954	79	12	the	the	DET
ajst-25954	79	13	same	same	ADJ
ajst-25954	79	14	dimension	dimension	NOUN
ajst-25954	79	15	,	,	PUNCT
ajst-25954	79	16	and	and	CCONJ
ajst-25954	79	17	the	the	DET
ajst-25954	79	18	convolution	convolution	NOUN
ajst-25954	79	19	is	be	AUX
ajst-25954	79	20	carried	carry	VERB
ajst-25954	79	21	out	out	ADP
ajst-25954	79	22	by	by	ADP
ajst-25954	79	23	5×5	5×5	NUM
ajst-25954	79	24	convolution	convolution	NOUN
ajst-25954	79	25	kernel	kernel	NOUN
ajst-25954	79	26	,	,	PUNCT
ajst-25954	79	27	which	which	PRON
ajst-25954	79	28	the	the	DET
ajst-25954	79	29	extracted	extract	VERB
ajst-25954	79	30	spatiotemporal	spatiotemporal	ADJ
ajst-25954	79	31	features	feature	NOUN
ajst-25954	79	32	are	be	AUX
ajst-25954	79	33	input	input	VERB
ajst-25954	79	34	the	the	DET
ajst-25954	79	35	fm	fm	PROPN
ajst-25954	79	36	layer	layer	NOUN
ajst-25954	79	37	to	to	PART
ajst-25954	79	38	capture	capture	VERB
ajst-25954	79	39	the	the	DET
ajst-25954	79	40	interaction	interaction	NOUN
ajst-25954	79	41	relationship	relationship	NOUN
ajst-25954	79	42	between	between	ADP
ajst-25954	79	43	the	the	DET
ajst-25954	79	44	features	feature	NOUN
ajst-25954	79	45	.	.	PUNCT
ajst-25954	80	1	through	through	ADP
ajst-25954	80	2	fusing	fuse	VERB
ajst-25954	80	3	multi	multi	ADJ
ajst-25954	80	4	-	-	ADJ
ajst-25954	80	5	scale	scale	ADJ
ajst-25954	80	6	feature	feature	NOUN
ajst-25954	80	7	information	information	NOUN
ajst-25954	80	8	,	,	PUNCT
ajst-25954	80	9	the	the	DET
ajst-25954	80	10	global	global	ADJ
ajst-25954	80	11	information	information	NOUN
ajst-25954	80	12	of	of	ADP
ajst-25954	80	13	electrode	electrode	NOUN
ajst-25954	80	14	channels	channel	NOUN
ajst-25954	80	15	can	can	AUX
ajst-25954	80	16	be	be	AUX
ajst-25954	80	17	captured	capture	VERB
ajst-25954	80	18	effectively	effectively	ADV
ajst-25954	80	19	,	,	PUNCT
ajst-25954	80	20	thus	thus	ADV
ajst-25954	80	21	improving	improve	VERB
ajst-25954	80	22	the	the	DET
ajst-25954	80	23	performance	performance	NOUN
ajst-25954	80	24	and	and	CCONJ
ajst-25954	80	25	robustness	robustness	NOUN
ajst-25954	80	26	of	of	ADP
ajst-25954	80	27	feature	feature	NOUN
ajst-25954	80	28	extraction	extraction	NOUN
ajst-25954	80	29	.	.	PUNCT
ajst-25954	81	1	1×1	1×1	ADJ
ajst-25954	81	2	3×3	3×3	NUM
ajst-25954	81	3	5×5	5×5	NUM
ajst-25954	81	4	7×7	7×7	NUM
ajst-25954	81	5	5×5	5×5	NUM
ajst-25954	81	6	flatten	flatten	ADJ
ajst-25954	81	7	 	 	SPACE
ajst-25954	81	8	layer	layer	NOUN
ajst-25954	81	9	fm	fm	PROPN
ajst-25954	81	10	 	 	SPACE
ajst-25954	81	11	layer	layer	NOUN
ajst-25954	81	12	figure	figure	NOUN
ajst-25954	81	13	2	2	NUM
ajst-25954	81	14	.	.	PUNCT
ajst-25954	82	1	multi	multi	ADJ
ajst-25954	82	2	-	-	ADJ
ajst-25954	82	3	scale	scale	ADJ
ajst-25954	82	4	feature	feature	NOUN
ajst-25954	82	5	merging	merge	VERB
ajst-25954	82	6	convolutional	convolutional	ADJ
ajst-25954	82	7	networks	network	NOUN
ajst-25954	82	8	.	.	PUNCT
ajst-25954	83	1	84	84	NUM
ajst-25954	83	2	2	2	NUM
ajst-25954	83	3	.	.	PUNCT
ajst-25954	83	4	spatiotemporal	spatiotemporal	ADJ
ajst-25954	83	5	factorization	factorization	NOUN
ajst-25954	83	6	machine	machine	NOUN
ajst-25954	83	7	spatiotemporal	spatiotemporal	ADJ
ajst-25954	83	8	features	feature	NOUN
ajst-25954	83	9	are	be	AUX
ajst-25954	83	10	extracted	extract	VERB
ajst-25954	83	11	from	from	ADP
ajst-25954	83	12	3d	3d	NUM
ajst-25954	83	13	feature	feature	NOUN
ajst-25954	83	14	matrix	matrix	NOUN
ajst-25954	83	15	,	,	PUNCT
ajst-25954	83	16	the	the	DET
ajst-25954	83	17	interaction	interaction	NOUN
ajst-25954	83	18	of	of	ADP
ajst-25954	83	19	feature	feature	NOUN
ajst-25954	83	20	combination	combination	NOUN
ajst-25954	83	21	and	and	CCONJ
ajst-25954	83	22	the	the	DET
ajst-25954	83	23	potential	potential	ADJ
ajst-25954	83	24	data	datum	NOUN
ajst-25954	83	25	sparsity	sparsity	NOUN
ajst-25954	83	26	of	of	ADP
ajst-25954	83	27	convolutional	convolutional	ADJ
ajst-25954	83	28	networks	network	NOUN
ajst-25954	83	29	are	be	AUX
ajst-25954	83	30	ignored	ignore	VERB
ajst-25954	83	31	.	.	PUNCT
ajst-25954	84	1	however	however	ADV
ajst-25954	84	2	,	,	PUNCT
ajst-25954	84	3	the	the	DET
ajst-25954	84	4	introduction	introduction	NOUN
ajst-25954	84	5	of	of	ADP
ajst-25954	84	6	spatiotemporal	spatiotemporal	ADJ
ajst-25954	84	7	factorization	factorization	NOUN
ajst-25954	84	8	machine	machine	NOUN
ajst-25954	84	9	can	can	AUX
ajst-25954	84	10	solve	solve	VERB
ajst-25954	84	11	this	this	DET
ajst-25954	84	12	problem	problem	NOUN
ajst-25954	84	13	skillfully	skillfully	ADV
ajst-25954	84	14	.	.	PUNCT
ajst-25954	85	1	rendle	rendle	VERB
ajst-25954	85	2	et	et	PROPN
ajst-25954	85	3	al	al	PROPN
ajst-25954	85	4	.	.	PROPN
ajst-25954	85	5	proposed	propose	VERB
ajst-25954	85	6	the	the	DET
ajst-25954	85	7	factorization	factorization	NOUN
ajst-25954	85	8	machine	machine	NOUN
ajst-25954	86	1	[	[	X
ajst-25954	86	2	25	25	NUM
ajst-25954	86	3	]	]	PUNCT
ajst-25954	86	4	,	,	PUNCT
ajst-25954	86	5	inspired	inspire	VERB
ajst-25954	86	6	by	by	ADP
ajst-25954	86	7	support	support	NOUN
ajst-25954	86	8	vector	vector	NOUN
ajst-25954	86	9	machines	machine	NOUN
ajst-25954	86	10	(	(	PUNCT
ajst-25954	86	11	svm	svm	PROPN
ajst-25954	86	12	)	)	PUNCT
ajst-25954	86	13	and	and	CCONJ
ajst-25954	86	14	matrix	matrix	NOUN
ajst-25954	86	15	factorization	factorization	NOUN
ajst-25954	86	16	techniques	technique	NOUN
ajst-25954	86	17	.	.	PUNCT
ajst-25954	87	1	fm	fm	PROPN
ajst-25954	87	2	is	be	AUX
ajst-25954	87	3	originally	originally	ADV
ajst-25954	87	4	introduced	introduce	VERB
ajst-25954	87	5	for	for	ADP
ajst-25954	87	6	collaborative	collaborative	ADJ
ajst-25954	87	7	filtering	filtering	NOUN
ajst-25954	87	8	,	,	PUNCT
ajst-25954	87	9	which	which	PRON
ajst-25954	87	10	has	have	AUX
ajst-25954	87	11	since	since	ADV
ajst-25954	87	12	been	be	AUX
ajst-25954	87	13	widely	widely	ADV
ajst-25954	87	14	used	use	VERB
ajst-25954	87	15	in	in	ADP
ajst-25954	87	16	recommendation	recommendation	NOUN
ajst-25954	87	17	systems	system	NOUN
ajst-25954	87	18	and	and	CCONJ
ajst-25954	87	19	other	other	ADJ
ajst-25954	87	20	fields	field	NOUN
ajst-25954	87	21	.	.	PUNCT
ajst-25954	88	1	the	the	DET
ajst-25954	88	2	core	core	ADJ
ajst-25954	88	3	idea	idea	NOUN
ajst-25954	88	4	of	of	ADP
ajst-25954	88	5	fm	fm	PROPN
ajst-25954	88	6	is	be	AUX
ajst-25954	88	7	to	to	PART
ajst-25954	88	8	learn	learn	VERB
ajst-25954	88	9	the	the	DET
ajst-25954	88	10	interaction	interaction	NOUN
ajst-25954	88	11	between	between	ADP
ajst-25954	88	12	features	feature	NOUN
ajst-25954	88	13	by	by	ADP
ajst-25954	88	14	factorizing	factorize	VERB
ajst-25954	88	15	features	feature	NOUN
ajst-25954	88	16	.	.	PUNCT
ajst-25954	89	1	compared	compare	VERB
ajst-25954	89	2	with	with	ADP
ajst-25954	89	3	the	the	DET
ajst-25954	89	4	traditional	traditional	ADJ
ajst-25954	89	5	linear	linear	PROPN
ajst-25954	89	6	model	model	NOUN
ajst-25954	89	7	,	,	PUNCT
ajst-25954	89	8	fm	fm	PROPN
ajst-25954	89	9	introduced	introduce	VERB
ajst-25954	89	10	the	the	DET
ajst-25954	89	11	concept	concept	NOUN
ajst-25954	89	12	of	of	ADP
ajst-25954	89	13	hidden	hidden	ADJ
ajst-25954	89	14	factors	factor	NOUN
ajst-25954	89	15	,	,	PUNCT
ajst-25954	89	16	which	which	PRON
ajst-25954	89	17	represents	represent	VERB
ajst-25954	89	18	the	the	DET
ajst-25954	89	19	associations	association	NOUN
ajst-25954	89	20	between	between	ADP
ajst-25954	89	21	each	each	DET
ajst-25954	89	22	feature	feature	NOUN
ajst-25954	89	23	by	by	ADP
ajst-25954	89	24	modeling	model	VERB
ajst-25954	89	25	feature	feature	NOUN
ajst-25954	89	26	combinations	combination	NOUN
ajst-25954	89	27	as	as	ADP
ajst-25954	89	28	the	the	DET
ajst-25954	89	29	inner	inner	ADJ
ajst-25954	89	30	product	product	NOUN
ajst-25954	89	31	of	of	ADP
ajst-25954	89	32	pairwise	pairwise	NOUN
ajst-25954	89	33	feature	feature	NOUN
ajst-25954	89	34	interactions	interaction	NOUN
ajst-25954	89	35	.	.	PUNCT
ajst-25954	90	1	this	this	PRON
ajst-25954	90	2	allows	allow	VERB
ajst-25954	90	3	fm	fm	NOUN
ajst-25954	90	4	to	to	PART
ajst-25954	90	5	better	well	ADV
ajst-25954	90	6	capture	capture	VERB
ajst-25954	90	7	higher	high	ADJ
ajst-25954	90	8	-	-	PUNCT
ajst-25954	90	9	order	order	NOUN
ajst-25954	90	10	relationships	relationship	NOUN
ajst-25954	90	11	between	between	ADP
ajst-25954	90	12	features	feature	NOUN
ajst-25954	90	13	,	,	PUNCT
ajst-25954	90	14	which	which	PRON
ajst-25954	90	15	improves	improve	VERB
ajst-25954	90	16	the	the	DET
ajst-25954	90	17	expressiveness	expressiveness	NOUN
ajst-25954	90	18	of	of	ADP
ajst-25954	90	19	the	the	DET
ajst-25954	90	20	model	model	NOUN
ajst-25954	90	21	.	.	PUNCT
ajst-25954	91	1	with	with	ADP
ajst-25954	91	2	the	the	DET
ajst-25954	91	3	introduction	introduction	NOUN
ajst-25954	91	4	of	of	ADP
ajst-25954	91	5	hidden	hidden	ADJ
ajst-25954	91	6	factors	factor	NOUN
ajst-25954	91	7	,	,	PUNCT
ajst-25954	91	8	fm	fm	PROPN
ajst-25954	91	9	can	can	AUX
ajst-25954	91	10	effectively	effectively	ADV
ajst-25954	91	11	solve	solve	VERB
ajst-25954	91	12	the	the	DET
ajst-25954	91	13	feature	feature	NOUN
ajst-25954	91	14	combination	combination	NOUN
ajst-25954	91	15	problem	problem	NOUN
ajst-25954	91	16	of	of	ADP
ajst-25954	91	17	high	high	ADJ
ajst-25954	91	18	-	-	PUNCT
ajst-25954	91	19	dimensional	dimensional	ADJ
ajst-25954	91	20	sparse	sparse	ADJ
ajst-25954	91	21	data	datum	NOUN
ajst-25954	91	22	,	,	PUNCT
ajst-25954	91	23	which	which	PRON
ajst-25954	91	24	has	have	VERB
ajst-25954	91	25	good	good	ADJ
ajst-25954	91	26	scalability	scalability	NOUN
ajst-25954	91	27	.	.	PUNCT
ajst-25954	92	1	the	the	DET
ajst-25954	92	2	fm	fm	PROPN
ajst-25954	92	3	layer	layer	NOUN
ajst-25954	92	4	is	be	AUX
ajst-25954	92	5	added	add	VERB
ajst-25954	92	6	to	to	ADP
ajst-25954	92	7	the	the	DET
ajst-25954	92	8	multi	multi	ADJ
ajst-25954	92	9	-	-	ADJ
ajst-25954	92	10	scale	scale	ADJ
ajst-25954	92	11	feature	feature	NOUN
ajst-25954	92	12	convolution	convolution	NOUN
ajst-25954	92	13	layer	layer	NOUN
ajst-25954	92	14	,	,	PUNCT
ajst-25954	92	15	which	which	PRON
ajst-25954	92	16	can	can	AUX
ajst-25954	92	17	effectively	effectively	ADV
ajst-25954	92	18	enhance	enhance	VERB
ajst-25954	92	19	the	the	DET
ajst-25954	92	20	spatiotemporal	spatiotemporal	ADJ
ajst-25954	92	21	feature	feature	NOUN
ajst-25954	92	22	interaction	interaction	NOUN
ajst-25954	92	23	and	and	CCONJ
ajst-25954	92	24	simplify	simplify	VERB
ajst-25954	92	25	parameter	parameter	NOUN
ajst-25954	92	26	updating	updating	NOUN
ajst-25954	92	27	.	.	PUNCT
ajst-25954	93	1	due	due	ADP
ajst-25954	93	2	to	to	ADP
ajst-25954	93	3	the	the	DET
ajst-25954	93	4	sparsity	sparsity	NOUN
ajst-25954	93	5	of	of	ADP
ajst-25954	93	6	convolutional	convolutional	ADJ
ajst-25954	93	7	networks	network	NOUN
ajst-25954	93	8	,	,	PUNCT
ajst-25954	93	9	the	the	DET
ajst-25954	93	10	parameter	parameter	NOUN
ajst-25954	93	11	estimation	estimation	NOUN
ajst-25954	93	12	of	of	ADP
ajst-25954	93	13	linear	linear	PROPN
ajst-25954	93	14	models	model	NOUN
ajst-25954	93	15	may	may	AUX
ajst-25954	93	16	become	become	VERB
ajst-25954	93	17	unstable	unstable	ADJ
ajst-25954	93	18	.	.	PUNCT
ajst-25954	94	1	in	in	ADP
ajst-25954	94	2	view	view	NOUN
ajst-25954	94	3	of	of	ADP
ajst-25954	94	4	this	this	PRON
ajst-25954	94	5	,	,	PUNCT
ajst-25954	94	6	the	the	DET
ajst-25954	94	7	most	most	ADV
ajst-25954	94	8	important	important	ADJ
ajst-25954	94	9	features	feature	NOUN
ajst-25954	94	10	can	can	AUX
ajst-25954	94	11	be	be	AUX
ajst-25954	94	12	selected	select	VERB
ajst-25954	94	13	through	through	ADP
ajst-25954	94	14	fm	fm	PROPN
ajst-25954	94	15	,	,	PUNCT
ajst-25954	94	16	or	or	CCONJ
ajst-25954	94	17	the	the	DET
ajst-25954	94	18	most	most	ADV
ajst-25954	94	19	informative	informative	ADJ
ajst-25954	94	20	features	feature	NOUN
ajst-25954	94	21	can	can	AUX
ajst-25954	94	22	be	be	AUX
ajst-25954	94	23	extracted	extract	VERB
ajst-25954	94	24	.	.	PUNCT
ajst-25954	95	1	in	in	ADP
ajst-25954	95	2	this	this	DET
ajst-25954	95	3	way	way	NOUN
ajst-25954	95	4	,	,	PUNCT
ajst-25954	95	5	the	the	DET
ajst-25954	95	6	correlation	correlation	NOUN
ajst-25954	95	7	between	between	ADP
ajst-25954	95	8	features	feature	NOUN
ajst-25954	95	9	can	can	AUX
ajst-25954	95	10	be	be	AUX
ajst-25954	95	11	reduced	reduce	VERB
ajst-25954	95	12	,	,	PUNCT
ajst-25954	95	13	thus	thus	ADV
ajst-25954	95	14	solving	solve	VERB
ajst-25954	95	15	the	the	DET
ajst-25954	95	16	problem	problem	NOUN
ajst-25954	95	17	of	of	ADP
ajst-25954	95	18	network	network	NOUN
ajst-25954	95	19	sparsity	sparsity	NOUN
ajst-25954	95	20	.	.	PUNCT
ajst-25954	96	1	in	in	ADP
ajst-25954	96	2	other	other	ADJ
ajst-25954	96	3	words	word	NOUN
ajst-25954	96	4	,	,	PUNCT
ajst-25954	96	5	interaction	interaction	NOUN
ajst-25954	96	6	items	item	NOUN
ajst-25954	96	7	in	in	ADP
ajst-25954	96	8	the	the	DET
ajst-25954	96	9	fm	fm	NOUN
ajst-25954	96	10	layer∑	layer∑	VERB
ajst-25954	96	11	∑	∑	PROPN
ajst-25954	96	12	〈	〈	PROPN
ajst-25954	96	13	v	v	NOUN
ajst-25954	96	14	,	,	PUNCT
ajst-25954	96	15	v	v	PRON
ajst-25954	96	16	〉	〉	NOUN
ajst-25954	96	17	x	x	NOUN
ajst-25954	96	18	∙	∙	NOUN
ajst-25954	96	19	x	x	PRON
ajst-25954	96	20	can	can	AUX
ajst-25954	96	21	learn	learn	VERB
ajst-25954	96	22	their	their	PRON
ajst-25954	96	23	interactions	interaction	NOUN
ajst-25954	96	24	between	between	ADP
ajst-25954	96	25	the	the	DET
ajst-25954	96	26	different	different	ADJ
ajst-25954	96	27	features	feature	NOUN
ajst-25954	96	28	obtained	obtain	VERB
ajst-25954	96	29	.	.	PUNCT
ajst-25954	97	1	therefore	therefore	ADV
ajst-25954	97	2	,	,	PUNCT
ajst-25954	97	3	fm	fm	PROPN
ajst-25954	97	4	captures	capture	VERB
ajst-25954	97	5	the	the	DET
ajst-25954	97	6	nonlinear	nonlinear	ADJ
ajst-25954	97	7	interactions	interaction	NOUN
ajst-25954	97	8	of	of	ADP
ajst-25954	97	9	secondorder	secondorder	ADJ
ajst-25954	97	10	factors	factor	NOUN
ajst-25954	97	11	between	between	ADP
ajst-25954	97	12	features	feature	NOUN
ajst-25954	97	13	while	while	SCONJ
ajst-25954	97	14	maintaining	maintain	VERB
ajst-25954	97	15	linear	linear	NOUN
ajst-25954	97	16	complexity	complexity	NOUN
ajst-25954	97	17	,	,	PUNCT
ajst-25954	97	18	the	the	DET
ajst-25954	97	19	fm	fm	NOUN
ajst-25954	97	20	plays	play	VERB
ajst-25954	97	21	a	a	DET
ajst-25954	97	22	key	key	ADJ
ajst-25954	97	23	advantage	advantage	NOUN
ajst-25954	97	24	in	in	ADP
ajst-25954	97	25	learning	learn	VERB
ajst-25954	97	26	feature	feature	NOUN
ajst-25954	97	27	interactions	interaction	NOUN
ajst-25954	97	28	.	.	PUNCT
ajst-25954	98	1	given	give	VERB
ajst-25954	98	2	a	a	DET
ajst-25954	98	3	real	real	ADV
ajst-25954	98	4	valued	value	VERB
ajst-25954	98	5	eigenvector	eigenvector	NOUN
ajst-25954	98	6	𝐱∈r𝑛	𝐱∈r𝑛	PROPN
ajst-25954	98	7	,	,	PUNCT
ajst-25954	98	8	the	the	DET
ajst-25954	98	9	fm	fm	PROPN
ajst-25954	98	10	model	model	NOUN
ajst-25954	98	11	can	can	AUX
ajst-25954	98	12	represent	represent	VERB
ajst-25954	98	13	(	(	PUNCT
ajst-25954	98	14	1	1	NUM
ajst-25954	98	15	)	)	PUNCT
ajst-25954	98	16	.	.	PUNCT
ajst-25954	99	1	𝐹	𝐹	PRON
ajst-25954	100	1	𝑥	𝑥	X
ajst-25954	101	1	𝜃	𝜃	NOUN
ajst-25954	101	2	𝜃	𝜃	PUNCT
ajst-25954	102	1	∙	∙	PROPN
ajst-25954	102	2	𝑦	𝑦	NOUN
ajst-25954	102	3	〈	〈	NOUN
ajst-25954	102	4	𝑤	𝑤	X
ajst-25954	102	5	,	,	PUNCT
ajst-25954	102	6	𝑤	𝑤	ADP
ajst-25954	102	7	〉	〉	PROPN
ajst-25954	102	8	𝑦	𝑦	NOUN
ajst-25954	102	9	∙	∙	PROPN
ajst-25954	102	10	𝑦	𝑦	NOUN
ajst-25954	102	11	,	,	PUNCT
ajst-25954	102	12	1	1	NUM
ajst-25954	102	13	where	where	SCONJ
ajst-25954	102	14	𝜃	𝜃	X
ajst-25954	102	15	∈r	∈r	NOUN
ajst-25954	102	16	is	be	AUX
ajst-25954	102	17	the	the	DET
ajst-25954	102	18	deviation	deviation	NOUN
ajst-25954	102	19	and	and	CCONJ
ajst-25954	102	20	𝜃∈rn	𝜃∈rn	VERB
ajst-25954	102	21	represents	represent	VERB
ajst-25954	102	22	the	the	DET
ajst-25954	102	23	linear	linear	ADJ
ajst-25954	102	24	interaction	interaction	NOUN
ajst-25954	102	25	of	of	ADP
ajst-25954	102	26	the	the	DET
ajst-25954	102	27	target	target	NOUN
ajst-25954	102	28	.	.	PUNCT
ajst-25954	103	1	the	the	DET
ajst-25954	103	2	inner	inner	ADJ
ajst-25954	103	3	product	product	NOUN
ajst-25954	103	4	term	term	NOUN
ajst-25954	103	5	〈	〈	NOUN
ajst-25954	103	6	𝑤	𝑤	NOUN
ajst-25954	103	7	,	,	PUNCT
ajst-25954	103	8	𝑤	𝑤	ADP
ajst-25954	103	9	〉	〉	NOUN
ajst-25954	103	10	captures	capture	VERB
ajst-25954	103	11	interactions	interaction	NOUN
ajst-25954	103	12	between	between	ADP
ajst-25954	103	13	variables	variable	NOUN
ajst-25954	103	14	,	,	PUNCT
ajst-25954	103	15	where	where	SCONJ
ajst-25954	103	16	each	each	DET
ajst-25954	103	17	w	w	PROPN
ajst-25954	103	18	∈r𝑘	∈r𝑘	ADJ
ajst-25954	103	19	is	be	AUX
ajst-25954	103	20	the	the	DET
ajst-25954	103	21	first	first	ADJ
ajst-25954	103	22	𝑖	𝑖	SYM
ajst-25954	103	23	vector	vector	NOUN
ajst-25954	103	24	of	of	ADP
ajst-25954	103	25	the	the	DET
ajst-25954	103	26	coefficient	coefficient	NOUN
ajst-25954	103	27	matrix	matrix	NOUN
ajst-25954	103	28	w	w	NOUN
ajst-25954	103	29	,	,	PUNCT
ajst-25954	103	30	and	and	CCONJ
ajst-25954	103	31	𝑘	𝑘	PRON
ajst-25954	103	32	is	be	AUX
ajst-25954	103	33	the	the	DET
ajst-25954	103	34	dimensional	dimensional	ADJ
ajst-25954	103	35	parameter	parameter	NOUN
ajst-25954	103	36	of	of	ADP
ajst-25954	103	37	the	the	DET
ajst-25954	103	38	auxiliary	auxiliary	ADJ
ajst-25954	103	39	vector	vector	NOUN
ajst-25954	103	40	.	.	PUNCT
ajst-25954	104	1	limiting	limit	VERB
ajst-25954	104	2	the	the	DET
ajst-25954	104	3	𝑘	𝑘	PROPN
ajst-25954	104	4	value	value	NOUN
ajst-25954	104	5	can	can	AUX
ajst-25954	104	6	improve	improve	VERB
ajst-25954	104	7	the	the	DET
ajst-25954	104	8	generalization	generalization	NOUN
ajst-25954	104	9	ability	ability	NOUN
ajst-25954	104	10	of	of	ADP
ajst-25954	104	11	the	the	DET
ajst-25954	104	12	model	model	NOUN
ajst-25954	104	13	to	to	ADP
ajst-25954	104	14	some	some	DET
ajst-25954	104	15	extent	extent	NOUN
ajst-25954	104	16	.	.	PUNCT
ajst-25954	105	1	as	as	SCONJ
ajst-25954	105	2	can	can	AUX
ajst-25954	105	3	be	be	AUX
ajst-25954	105	4	seen	see	VERB
ajst-25954	105	5	from	from	ADP
ajst-25954	105	6	equation	equation	NOUN
ajst-25954	105	7	(	(	PUNCT
ajst-25954	105	8	1	1	NUM
ajst-25954	105	9	)	)	PUNCT
ajst-25954	105	10	,	,	PUNCT
ajst-25954	105	11	the	the	DET
ajst-25954	105	12	complexity	complexity	NOUN
ajst-25954	105	13	of	of	ADP
ajst-25954	105	14	the	the	DET
ajst-25954	105	15	algorithm	algorithm	NOUN
ajst-25954	105	16	is	be	AUX
ajst-25954	105	17	𝑂(𝑘𝑛2	𝑂(𝑘𝑛2	NOUN
ajst-25954	105	18	)	)	PUNCT
ajst-25954	105	19	.	.	PUNCT
ajst-25954	106	1	however	however	ADV
ajst-25954	106	2	,	,	PUNCT
ajst-25954	106	3	in	in	ADP
ajst-25954	106	4	practical	practical	ADJ
ajst-25954	106	5	applications	application	NOUN
ajst-25954	106	6	,	,	PUNCT
ajst-25954	106	7	the	the	DET
ajst-25954	106	8	value	value	NOUN
ajst-25954	106	9	of	of	ADP
ajst-25954	106	10	n	n	NUM
ajst-25954	106	11	is	be	AUX
ajst-25954	106	12	usually	usually	ADV
ajst-25954	106	13	too	too	ADV
ajst-25954	106	14	large	large	ADJ
ajst-25954	106	15	,	,	PUNCT
ajst-25954	106	16	which	which	PRON
ajst-25954	106	17	results	result	VERB
ajst-25954	106	18	in	in	ADP
ajst-25954	106	19	excessive	excessive	ADJ
ajst-25954	106	20	computational	computational	ADJ
ajst-25954	106	21	complexity	complexity	NOUN
ajst-25954	106	22	.	.	PUNCT
ajst-25954	107	1	therefore	therefore	ADV
ajst-25954	107	2	,	,	PUNCT
ajst-25954	107	3	in	in	ADP
ajst-25954	107	4	order	order	NOUN
ajst-25954	107	5	to	to	PART
ajst-25954	107	6	alleviate	alleviate	VERB
ajst-25954	107	7	this	this	DET
ajst-25954	107	8	situation	situation	NOUN
ajst-25954	107	9	,	,	PUNCT
ajst-25954	107	10	the	the	DET
ajst-25954	107	11	complexity	complexity	NOUN
ajst-25954	107	12	of	of	ADP
ajst-25954	107	13	fm	fm	PROPN
ajst-25954	107	14	algorithm	algorithm	PROPN
ajst-25954	107	15	can	can	AUX
ajst-25954	107	16	be	be	AUX
ajst-25954	107	17	reduced	reduce	VERB
ajst-25954	107	18	to	to	ADP
ajst-25954	107	19	𝑘𝑛	𝑘𝑛	X
ajst-25954	107	20	by	by	ADP
ajst-25954	107	21	a	a	DET
ajst-25954	107	22	series	series	NOUN
ajst-25954	107	23	of	of	ADP
ajst-25954	107	24	identity	identity	NOUN
ajst-25954	107	25	transformations	transformation	NOUN
ajst-25954	107	26	,	,	PUNCT
ajst-25954	107	27	which	which	PRON
ajst-25954	107	28	can	can	AUX
ajst-25954	107	29	reduce	reduce	VERB
ajst-25954	107	30	the	the	DET
ajst-25954	107	31	computational	computational	ADJ
ajst-25954	107	32	pressure	pressure	NOUN
ajst-25954	107	33	to	to	ADP
ajst-25954	107	34	a	a	DET
ajst-25954	107	35	certain	certain	ADJ
ajst-25954	107	36	extent	extent	NOUN
ajst-25954	107	37	.	.	PUNCT
ajst-25954	108	1	the	the	DET
ajst-25954	108	2	expression	expression	NOUN
ajst-25954	108	3	can	can	AUX
ajst-25954	108	4	be	be	AUX
ajst-25954	108	5	re	re	VERB
ajst-25954	108	6	-	-	VERB
ajst-25954	108	7	deduced	deduce	VERB
ajst-25954	108	8	as	as	ADP
ajst-25954	108	9	:	:	PUNCT
ajst-25954	108	10	𝐹	𝐹	PROPN
ajst-25954	108	11	𝑥	𝑥	X
ajst-25954	108	12	𝜃	𝜃	NOUN
ajst-25954	108	13	𝜃	𝜃	PUNCT
ajst-25954	108	14	∙	∙	NOUN
ajst-25954	108	15	𝑦	𝑦	NUM
ajst-25954	108	16	1	1	NUM
ajst-25954	108	17	2	2	NUM
ajst-25954	108	18	𝑤	𝑤	ADP
ajst-25954	108	19	𝑦	𝑦	PROPN
ajst-25954	108	20	𝑤	𝑤	ADP
ajst-25954	108	21	𝑦	𝑦	NOUN
ajst-25954	108	22	2	2	NUM
ajst-25954	108	23	where	where	SCONJ
ajst-25954	108	24	w𝑖𝑓	w𝑖𝑓	NOUN
ajst-25954	108	25	represents	represent	VERB
ajst-25954	108	26	the	the	DET
ajst-25954	108	27	element	element	NOUN
ajst-25954	108	28	of	of	ADP
ajst-25954	108	29	w𝑖.	w𝑖.	NOUN
ajst-25954	108	30	through	through	ADP
ajst-25954	108	31	the	the	DET
ajst-25954	108	32	identity	identity	NOUN
ajst-25954	108	33	transformation	transformation	NOUN
ajst-25954	108	34	,	,	PUNCT
ajst-25954	108	35	the	the	DET
ajst-25954	108	36	complexity	complexity	NOUN
ajst-25954	108	37	of	of	ADP
ajst-25954	108	38	the	the	DET
ajst-25954	108	39	model	model	NOUN
ajst-25954	108	40	is	be	AUX
ajst-25954	108	41	reduced	reduce	VERB
ajst-25954	108	42	to	to	PART
ajst-25954	108	43	𝑂(𝑘𝑛	𝑂(𝑘𝑛	VERB
ajst-25954	108	44	)	)	PUNCT
ajst-25954	108	45	.	.	PUNCT
ajst-25954	109	1	given	give	VERB
ajst-25954	109	2	that	that	SCONJ
ajst-25954	109	3	the	the	DET
ajst-25954	109	4	number	number	NOUN
ajst-25954	109	5	of	of	ADP
ajst-25954	109	6	input	input	NOUN
ajst-25954	109	7	features	feature	NOUN
ajst-25954	109	8	is	be	AUX
ajst-25954	109	9	n	n	PRON
ajst-25954	109	10	,	,	PUNCT
ajst-25954	109	11	the	the	DET
ajst-25954	109	12	number	number	NOUN
ajst-25954	109	13	of	of	ADP
ajst-25954	109	14	neurons	neuron	NOUN
ajst-25954	109	15	in	in	ADP
ajst-25954	109	16	the	the	DET
ajst-25954	109	17	fm	fm	NOUN
ajst-25954	109	18	layer	layer	NOUN
ajst-25954	109	19	is	be	AUX
ajst-25954	109	20	q	q	ADJ
ajst-25954	109	21	and	and	CCONJ
ajst-25954	109	22	the	the	DET
ajst-25954	109	23	number	number	NOUN
ajst-25954	109	24	of	of	ADP
ajst-25954	109	25	neurons	neuron	NOUN
ajst-25954	109	26	in	in	ADP
ajst-25954	109	27	the	the	DET
ajst-25954	109	28	output	output	NOUN
ajst-25954	109	29	layer	layer	NOUN
ajst-25954	109	30	is	be	AUX
ajst-25954	109	31	o.	o.	ADJ
ajst-25954	109	32	then	then	ADV
ajst-25954	109	33	,	,	PUNCT
ajst-25954	109	34	by	by	ADP
ajst-25954	109	35	extracting	extract	VERB
ajst-25954	109	36	the	the	DET
ajst-25954	109	37	nonlinear	nonlinear	ADJ
ajst-25954	109	38	features	feature	NOUN
ajst-25954	109	39	of	of	ADP
ajst-25954	109	40	the	the	DET
ajst-25954	109	41	fm	fm	PROPN
ajst-25954	109	42	layer	layer	NOUN
ajst-25954	109	43	,	,	PUNCT
ajst-25954	109	44	the	the	DET
ajst-25954	109	45	output	output	NOUN
ajst-25954	109	46	is	be	AUX
ajst-25954	109	47	obtained	obtain	VERB
ajst-25954	109	48	:	:	PUNCT
ajst-25954	109	49	𝑦	𝑦	NUM
ajst-25954	109	50	𝑓	𝑓	PRON
ajst-25954	109	51	1	1	NUM
ajst-25954	109	52	2	2	NUM
ajst-25954	109	53	𝑤	𝑤	ADP
ajst-25954	109	54	𝑦	𝑦	NOUN
ajst-25954	109	55	𝑤	𝑤	ADP
ajst-25954	109	56	𝑦	𝑦	NOUN
ajst-25954	109	57	3	3	NUM
ajst-25954	109	58	where	where	SCONJ
ajst-25954	109	59	j=1,2,3,	j=1,2,3,	NOUN
ajst-25954	109	60	...	...	PUNCT
ajst-25954	109	61	,q	,q	PUNCT
ajst-25954	109	62	,	,	PUNCT
ajst-25954	109	63	w𝑖𝑙∈𝑅	w𝑖𝑙∈𝑅	PROPN
ajst-25954	109	64	is	be	AUX
ajst-25954	109	65	the	the	DET
ajst-25954	109	66	undetermined	undetermined	ADJ
ajst-25954	109	67	weight	weight	NOUN
ajst-25954	109	68	corresponding	correspond	VERB
ajst-25954	109	69	to	to	ADP
ajst-25954	109	70	𝑦	𝑦	PRON
ajst-25954	109	71	,	,	PUNCT
ajst-25954	109	72	the	the	DET
ajst-25954	109	73	auxiliary	auxiliary	ADJ
ajst-25954	109	74	vector	vector	NOUN
ajst-25954	109	75	w𝑖	w𝑖	NOUN
ajst-25954	109	76	is	be	AUX
ajst-25954	109	77	the	the	DET
ajst-25954	109	78	𝑘	𝑘	PROPN
ajst-25954	109	79	dimension	dimension	NOUN
ajst-25954	109	80	and	and	CCONJ
ajst-25954	109	81	f	f	PROPN
ajst-25954	109	82	represents	represent	VERB
ajst-25954	109	83	the	the	DET
ajst-25954	109	84	activation	activation	NOUN
ajst-25954	109	85	function	function	NOUN
ajst-25954	109	86	.	.	PUNCT
ajst-25954	110	1	figure	figure	VERB
ajst-25954	110	2	3	3	NUM
ajst-25954	110	3	.	.	PUNCT
ajst-25954	110	4	eeg	eeg	PROPN
ajst-25954	110	5	spatial	spatial	ADJ
ajst-25954	110	6	and	and	CCONJ
ajst-25954	110	7	temporal	temporal	ADJ
ajst-25954	110	8	feature	feature	NOUN
ajst-25954	110	9	extraction	extraction	NOUN
ajst-25954	110	10	.	.	PUNCT
ajst-25954	111	1	3	3	X
ajst-25954	111	2	.	.	X
ajst-25954	111	3	parallel	parallel	ADJ
ajst-25954	111	4	channel	channel	NOUN
ajst-25954	111	5	feature	feature	NOUN
ajst-25954	111	6	weighted	weight	VERB
ajst-25954	111	7	network	network	NOUN
ajst-25954	111	8	(	(	PUNCT
ajst-25954	111	9	pcfwnet	pcfwnet	PROPN
ajst-25954	111	10	)	)	PUNCT
ajst-25954	111	11	the	the	DET
ajst-25954	111	12	advanced	advanced	ADJ
ajst-25954	111	13	features	feature	NOUN
ajst-25954	111	14	extracted	extract	VERB
ajst-25954	111	15	by	by	ADP
ajst-25954	111	16	multi	multi	ADJ
ajst-25954	111	17	-	-	ADJ
ajst-25954	111	18	scale	scale	ADJ
ajst-25954	111	19	feature	feature	NOUN
ajst-25954	111	20	convolution	convolution	NOUN
ajst-25954	111	21	and	and	CCONJ
ajst-25954	111	22	spatiotemporal	spatiotemporal	ADJ
ajst-25954	111	23	factor	factor	NOUN
ajst-25954	111	24	factorization	factorization	NOUN
ajst-25954	111	25	,	,	PUNCT
ajst-25954	111	26	which	which	PRON
ajst-25954	111	27	fully	fully	ADV
ajst-25954	111	28	contain	contain	VERB
ajst-25954	111	29	the	the	DET
ajst-25954	111	30	sequence	sequence	NOUN
ajst-25954	111	31	of	of	ADP
ajst-25954	111	32	channels	channel	NOUN
ajst-25954	111	33	and	and	CCONJ
ajst-25954	111	34	the	the	DET
ajst-25954	111	35	interaction	interaction	NOUN
ajst-25954	111	36	information	information	NOUN
ajst-25954	111	37	between	between	ADP
ajst-25954	111	38	features	feature	NOUN
ajst-25954	111	39	.	.	PUNCT
ajst-25954	112	1	based	base	VERB
ajst-25954	112	2	on	on	ADP
ajst-25954	112	3	this	this	DET
ajst-25954	112	4	feature	feature	NOUN
ajst-25954	112	5	,	,	PUNCT
ajst-25954	112	6	the	the	DET
ajst-25954	112	7	method	method	NOUN
ajst-25954	112	8	of	of	ADP
ajst-25954	112	9	epileptic	epileptic	ADJ
ajst-25954	112	10	eeg	eeg	NOUN
ajst-25954	112	11	signal	signal	NOUN
ajst-25954	112	12	extraction	extraction	NOUN
ajst-25954	112	13	feature	feature	NOUN
ajst-25954	112	14	map	map	NOUN
ajst-25954	112	15	[	[	X
ajst-25954	112	16	26	26	NUM
ajst-25954	112	17	]	]	PUNCT
ajst-25954	112	18	is	be	AUX
ajst-25954	112	19	introduced	introduce	VERB
ajst-25954	112	20	,	,	PUNCT
ajst-25954	112	21	and	and	CCONJ
ajst-25954	112	22	the	the	DET
ajst-25954	112	23	spatiotemporal	spatiotemporal	ADJ
ajst-25954	112	24	feature	feature	NOUN
ajst-25954	112	25	extraction	extraction	NOUN
ajst-25954	112	26	maps	map	NOUN
ajst-25954	112	27	of	of	ADP
ajst-25954	112	28	epilepsy	epilepsy	ADJ
ajst-25954	112	29	eeg	eeg	NOUN
ajst-25954	112	30	signal	signal	NOUN
ajst-25954	112	31	are	be	AUX
ajst-25954	112	32	obtained	obtain	VERB
ajst-25954	112	33	,	,	PUNCT
ajst-25954	112	34	as	as	SCONJ
ajst-25954	112	35	shown	show	VERB
ajst-25954	112	36	in	in	ADP
ajst-25954	112	37	fig	fig	NOUN
ajst-25954	112	38	.	.	PUNCT
ajst-25954	113	1	3	3	X
ajst-25954	113	2	.	.	X
ajst-25954	113	3	the	the	DET
ajst-25954	113	4	area	area	NOUN
ajst-25954	113	5	where	where	SCONJ
ajst-25954	113	6	the	the	DET
ajst-25954	113	7	electrode	electrode	NOUN
ajst-25954	113	8	channel	channel	NOUN
ajst-25954	113	9	is	be	AUX
ajst-25954	113	10	active	active	ADJ
ajst-25954	113	11	,	,	PUNCT
ajst-25954	113	12	it	it	PRON
ajst-25954	113	13	is	be	AUX
ajst-25954	113	14	also	also	ADV
ajst-25954	113	15	the	the	DET
ajst-25954	113	16	area	area	NOUN
ajst-25954	113	17	with	with	ADP
ajst-25954	113	18	high	high	ADJ
ajst-25954	113	19	attention	attention	NOUN
ajst-25954	113	20	weight	weight	NOUN
ajst-25954	113	21	.	.	PUNCT
ajst-25954	114	1	according	accord	VERB
ajst-25954	114	2	to	to	ADP
ajst-25954	114	3	the	the	DET
ajst-25954	114	4	energy	energy	NOUN
ajst-25954	114	5	distribution	distribution	NOUN
ajst-25954	114	6	in	in	ADP
ajst-25954	114	7	the	the	DET
ajst-25954	114	8	eeg	eeg	NOUN
ajst-25954	114	9	time	time	NOUN
ajst-25954	114	10	domain	domain	NOUN
ajst-25954	114	11	and	and	CCONJ
ajst-25954	114	12	channel	channel	NOUN
ajst-25954	114	13	location	location	NOUN
ajst-25954	114	14	,	,	PUNCT
ajst-25954	114	15	the	the	DET
ajst-25954	114	16	extracted	extract	VERB
ajst-25954	114	17	features	feature	NOUN
ajst-25954	114	18	are	be	AUX
ajst-25954	114	19	input	input	NOUN
ajst-25954	114	20	into	into	ADP
ajst-25954	114	21	the	the	DET
ajst-25954	114	22	pcfwnet	pcfwnet	NOUN
ajst-25954	114	23	,	,	PUNCT
ajst-25954	114	24	and	and	CCONJ
ajst-25954	114	25	the	the	DET
ajst-25954	114	26	interactive	interactive	ADJ
ajst-25954	114	27	85	85	NUM
ajst-25954	114	28	information	information	NOUN
ajst-25954	114	29	and	and	CCONJ
ajst-25954	114	30	global	global	ADJ
ajst-25954	114	31	features	feature	NOUN
ajst-25954	114	32	are	be	AUX
ajst-25954	114	33	obtained	obtain	VERB
ajst-25954	114	34	by	by	ADP
ajst-25954	114	35	dynamically	dynamically	ADV
ajst-25954	114	36	adjusting	adjust	VERB
ajst-25954	114	37	channel	channel	NOUN
ajst-25954	114	38	weights	weight	NOUN
ajst-25954	114	39	in	in	ADP
ajst-25954	114	40	time	time	NOUN
ajst-25954	114	41	.	.	PUNCT
ajst-25954	115	1	the	the	DET
ajst-25954	115	2	network	network	NOUN
ajst-25954	115	3	consists	consist	VERB
ajst-25954	115	4	of	of	ADP
ajst-25954	115	5	a	a	DET
ajst-25954	115	6	multi	multi	ADJ
ajst-25954	115	7	-	-	ADJ
ajst-25954	115	8	scale	scale	ADJ
ajst-25954	115	9	inception	inception	NOUN
ajst-25954	115	10	module	module	NOUN
ajst-25954	115	11	and	and	CCONJ
ajst-25954	115	12	an	an	DET
ajst-25954	115	13	eca	eca	NOUN
ajst-25954	115	14	module	module	NOUN
ajst-25954	115	15	,	,	PUNCT
ajst-25954	115	16	as	as	SCONJ
ajst-25954	115	17	shown	show	VERB
ajst-25954	115	18	in	in	ADP
ajst-25954	115	19	fig	fig	NOUN
ajst-25954	115	20	.	.	PUNCT
ajst-25954	116	1	4	4	X
ajst-25954	116	2	.	.	X
ajst-25954	116	3	ecanet	ecanet	NOUN
ajst-25954	116	4	is	be	AUX
ajst-25954	116	5	an	an	DET
ajst-25954	116	6	efficient	efficient	ADJ
ajst-25954	116	7	channel	channel	NOUN
ajst-25954	116	8	attention	attention	NOUN
ajst-25954	116	9	network	network	NOUN
ajst-25954	116	10	,	,	PUNCT
ajst-25954	116	11	the	the	DET
ajst-25954	116	12	structure	structure	NOUN
ajst-25954	116	13	of	of	ADP
ajst-25954	116	14	which	which	PRON
ajst-25954	116	15	is	be	AUX
ajst-25954	116	16	shown	show	VERB
ajst-25954	116	17	in	in	ADP
ajst-25954	116	18	fig	fig	NOUN
ajst-25954	116	19	.	.	PUNCT
ajst-25954	117	1	4	4	X
ajst-25954	117	2	.	.	X
ajst-25954	117	3	eca	eca	NOUN
ajst-25954	117	4	channels	channel	NOUN
ajst-25954	117	5	use	use	VERB
ajst-25954	117	6	each	each	DET
ajst-25954	117	7	channel	channel	NOUN
ajst-25954	117	8	and	and	CCONJ
ajst-25954	117	9	k	k	NOUN
ajst-25954	117	10	-	-	PUNCT
ajst-25954	117	11	nearest	near	ADJ
ajst-25954	117	12	neighbor	neighbor	NOUN
ajst-25954	117	13	(	(	PUNCT
ajst-25954	117	14	knn	knn	PROPN
ajst-25954	117	15	)	)	PUNCT
ajst-25954	117	16	method	method	NOUN
ajst-25954	117	17	to	to	PART
ajst-25954	117	18	capture	capture	VERB
ajst-25954	117	19	interaction	interaction	NOUN
ajst-25954	117	20	information	information	NOUN
ajst-25954	117	21	between	between	ADP
ajst-25954	117	22	part	part	NOUN
ajst-25954	117	23	cross	cross	ADJ
ajst-25954	117	24	-	-	ADJ
ajst-25954	117	25	channel	channel	ADJ
ajst-25954	117	26	features	feature	NOUN
ajst-25954	117	27	.	.	PUNCT
ajst-25954	118	1	by	by	ADP
ajst-25954	118	2	performing	perform	VERB
ajst-25954	118	3	1d	1d	NUM
ajst-25954	118	4	convolution	convolution	NOUN
ajst-25954	118	5	of	of	ADP
ajst-25954	118	6	size	size	NOUN
ajst-25954	118	7	k	k	PROPN
ajst-25954	118	8	,	,	PUNCT
ajst-25954	118	9	the	the	DET
ajst-25954	118	10	strategy	strategy	NOUN
ajst-25954	118	11	of	of	ADP
ajst-25954	118	12	part	part	NOUN
ajst-25954	118	13	cross	cross	ADJ
ajst-25954	118	14	-	-	ADJ
ajst-25954	118	15	channel	channel	ADJ
ajst-25954	118	16	interaction	interaction	NOUN
ajst-25954	118	17	without	without	ADP
ajst-25954	118	18	reducing	reduce	VERB
ajst-25954	118	19	dimension	dimension	NOUN
ajst-25954	118	20	can	can	AUX
ajst-25954	118	21	be	be	AUX
ajst-25954	118	22	effectively	effectively	ADV
ajst-25954	118	23	implemented	implement	VERB
ajst-25954	118	24	.	.	PUNCT
ajst-25954	119	1	given	give	VERB
ajst-25954	119	2	an	an	DET
ajst-25954	119	3	input	input	NOUN
ajst-25954	119	4	x∈rh*w*c	x∈rh*w*c	NUM
ajst-25954	119	5	,	,	PUNCT
ajst-25954	119	6	it	it	PRON
ajst-25954	119	7	becomes	become	VERB
ajst-25954	119	8	x∈r1	x∈r1	NOUN
ajst-25954	119	9	*	*	PUNCT
ajst-25954	119	10	1*c	1*c	NUM
ajst-25954	119	11	through	through	ADP
ajst-25954	119	12	a	a	DET
ajst-25954	119	13	global	global	ADJ
ajst-25954	119	14	average	average	ADJ
ajst-25954	119	15	pooling	pool	VERB
ajst-25954	119	16	layer	layer	NOUN
ajst-25954	119	17	(	(	PUNCT
ajst-25954	119	18	gap	gap	NOUN
ajst-25954	119	19	)	)	PUNCT
ajst-25954	119	20	.	.	PUNCT
ajst-25954	120	1	in	in	ADP
ajst-25954	120	2	order	order	NOUN
ajst-25954	120	3	to	to	PART
ajst-25954	120	4	adjust	adjust	VERB
ajst-25954	120	5	the	the	DET
ajst-25954	120	6	resulting	result	VERB
ajst-25954	120	7	feature	feature	NOUN
ajst-25954	120	8	map	map	NOUN
ajst-25954	120	9	y	y	PROPN
ajst-25954	120	10	to	to	ADP
ajst-25954	120	11	the	the	DET
ajst-25954	120	12	shape	shape	NOUN
ajst-25954	120	13	required	require	VERB
ajst-25954	120	14	for	for	ADP
ajst-25954	120	15	subsequent	subsequent	ADJ
ajst-25954	120	16	convolution	convolution	NOUN
ajst-25954	120	17	operations	operation	NOUN
ajst-25954	120	18	,	,	PUNCT
ajst-25954	120	19	it	it	PRON
ajst-25954	120	20	becomes	become	VERB
ajst-25954	120	21	x∈r1*c	x∈r1*c	PROPN
ajst-25954	120	22	,	,	PUNCT
ajst-25954	120	23	after	after	ADP
ajst-25954	120	24	1d	1d	NUM
ajst-25954	120	25	convolution	convolution	NOUN
ajst-25954	120	26	,	,	PUNCT
ajst-25954	120	27	the	the	DET
ajst-25954	120	28	weight	weight	NOUN
ajst-25954	120	29	of	of	ADP
ajst-25954	120	30	1d	1d	NUM
ajst-25954	120	31	convolution	convolution	NOUN
ajst-25954	120	32	is	be	AUX
ajst-25954	120	33	:	:	PUNCT
ajst-25954	120	34	𝑊	𝑊	VERB
ajst-25954	120	35	𝜎	𝜎	NOUN
ajst-25954	120	36	𝑊	𝑊	NOUN
ajst-25954	120	37	𝑦	𝑦	NOUN
ajst-25954	120	38	,	,	PUNCT
ajst-25954	120	39	𝑦	𝑦	NOUN
ajst-25954	120	40	𝜖𝜑	𝜖𝜑	ADV
ajst-25954	120	41	4	4	NUM
ajst-25954	120	42	where	where	SCONJ
ajst-25954	120	43	the	the	DET
ajst-25954	120	44	𝜑	𝜑	NOUN
ajst-25954	120	45	represents	represent	VERB
ajst-25954	120	46	the	the	DET
ajst-25954	120	47	set	set	NOUN
ajst-25954	120	48	of	of	ADP
ajst-25954	120	49	k	k	PROPN
ajst-25954	120	50	adjacent	adjacent	ADJ
ajst-25954	120	51	channels	channel	NOUN
ajst-25954	120	52	of	of	ADP
ajst-25954	120	53	𝑦	𝑦	NOUN
ajst-25954	120	54	,	,	PUNCT
ajst-25954	120	55	which	which	PRON
ajst-25954	120	56	the	the	DET
ajst-25954	120	57	feature	feature	NOUN
ajst-25954	120	58	interaction	interaction	NOUN
ajst-25954	120	59	between	between	ADP
ajst-25954	120	60	channels	channel	NOUN
ajst-25954	120	61	is	be	AUX
ajst-25954	120	62	realized	realize	VERB
ajst-25954	120	63	by	by	ADP
ajst-25954	120	64	1d	1d	NUM
ajst-25954	120	65	convolution	convolution	NOUN
ajst-25954	120	66	of	of	ADP
ajst-25954	120	67	size	size	NOUN
ajst-25954	120	68	k.	k.	PROPN
ajst-25954	120	69	practice	practice	PROPN
ajst-25954	120	70	has	have	AUX
ajst-25954	120	71	proved	prove	VERB
ajst-25954	120	72	that	that	SCONJ
ajst-25954	120	73	appropriate	appropriate	ADJ
ajst-25954	120	74	cross	cross	ADJ
ajst-25954	120	75	-	-	ADJ
ajst-25954	120	76	channel	channel	ADJ
ajst-25954	120	77	interaction	interaction	NOUN
ajst-25954	120	78	can	can	AUX
ajst-25954	120	79	significantly	significantly	ADV
ajst-25954	120	80	reduce	reduce	VERB
ajst-25954	120	81	the	the	DET
ajst-25954	120	82	complexity	complexity	NOUN
ajst-25954	120	83	of	of	ADP
ajst-25954	120	84	the	the	DET
ajst-25954	120	85	model	model	NOUN
ajst-25954	120	86	,	,	PUNCT
ajst-25954	120	87	which	which	PRON
ajst-25954	120	88	maintain	maintain	VERB
ajst-25954	120	89	the	the	DET
ajst-25954	120	90	performance	performance	NOUN
ajst-25954	120	91	of	of	ADP
ajst-25954	120	92	the	the	DET
ajst-25954	120	93	model	model	NOUN
ajst-25954	120	94	.	.	PUNCT
ajst-25954	121	1	among	among	ADP
ajst-25954	121	2	them	they	PRON
ajst-25954	121	3	,	,	PUNCT
ajst-25954	121	4	gap	gap	NOUN
ajst-25954	121	5	can	can	AUX
ajst-25954	121	6	carry	carry	VERB
ajst-25954	121	7	out	out	ADP
ajst-25954	121	8	global	global	ADJ
ajst-25954	121	9	average	average	ADJ
ajst-25954	121	10	pooling	pooling	NOUN
ajst-25954	121	11	of	of	ADP
ajst-25954	121	12	input	input	NOUN
ajst-25954	121	13	feature	feature	NOUN
ajst-25954	121	14	graphs	graph	NOUN
ajst-25954	121	15	without	without	ADP
ajst-25954	121	16	dimensionality	dimensionality	NOUN
ajst-25954	121	17	reduction	reduction	NOUN
ajst-25954	121	18	,	,	PUNCT
ajst-25954	121	19	the	the	DET
ajst-25954	121	20	number	number	NOUN
ajst-25954	121	21	of	of	ADP
ajst-25954	121	22	channel	channel	NOUN
ajst-25954	121	23	dimensions	dimension	NOUN
ajst-25954	121	24	remains	remain	VERB
ajst-25954	121	25	unchanged	unchanged	ADJ
ajst-25954	121	26	,	,	PUNCT
ajst-25954	121	27	and	and	CCONJ
ajst-25954	121	28	the	the	DET
ajst-25954	121	29	spatial	spatial	ADJ
ajst-25954	121	30	dimension	dimension	NOUN
ajst-25954	121	31	is	be	AUX
ajst-25954	121	32	compressed	compress	VERB
ajst-25954	121	33	to	to	ADP
ajst-25954	121	34	1	1	NUM
ajst-25954	121	35	.	.	PUNCT
ajst-25954	122	1	the	the	DET
ajst-25954	122	2	convolution	convolution	NOUN
ajst-25954	122	3	kernel	kernel	PROPN
ajst-25954	122	4	size	size	NOUN
ajst-25954	122	5	k	k	PROPN
ajst-25954	122	6	represents	represent	VERB
ajst-25954	122	7	the	the	DET
ajst-25954	122	8	coverage	coverage	NOUN
ajst-25954	122	9	of	of	ADP
ajst-25954	122	10	part	part	NOUN
ajst-25954	122	11	cross	cross	ADJ
ajst-25954	122	12	-	-	ADJ
ajst-25954	122	13	channel	channel	ADJ
ajst-25954	122	14	interactions	interaction	NOUN
ajst-25954	122	15	.	.	PUNCT
ajst-25954	123	1	this	this	DET
ajst-25954	123	2	mechanism	mechanism	NOUN
ajst-25954	123	3	helps	help	VERB
ajst-25954	123	4	to	to	PART
ajst-25954	123	5	enhance	enhance	VERB
ajst-25954	123	6	the	the	DET
ajst-25954	123	7	interaction	interaction	NOUN
ajst-25954	123	8	between	between	ADP
ajst-25954	123	9	channels	channel	NOUN
ajst-25954	123	10	more	more	ADV
ajst-25954	123	11	effectively	effectively	ADV
ajst-25954	123	12	while	while	SCONJ
ajst-25954	123	13	maintaining	maintain	VERB
ajst-25954	123	14	the	the	DET
ajst-25954	123	15	correlation	correlation	NOUN
ajst-25954	123	16	between	between	ADP
ajst-25954	123	17	channels	channel	NOUN
ajst-25954	123	18	,	,	PUNCT
ajst-25954	123	19	in	in	ADP
ajst-25954	123	20	order	order	NOUN
ajst-25954	123	21	to	to	PART
ajst-25954	123	22	improve	improve	VERB
ajst-25954	123	23	the	the	DET
ajst-25954	123	24	network	network	NOUN
ajst-25954	123	25	's	's	PART
ajst-25954	123	26	expressive	expressive	ADJ
ajst-25954	123	27	ability	ability	NOUN
ajst-25954	123	28	and	and	CCONJ
ajst-25954	123	29	performance	performance	NOUN
ajst-25954	123	30	.	.	PUNCT
ajst-25954	124	1	the	the	DET
ajst-25954	124	2	inception	inception	NOUN
ajst-25954	124	3	module	module	NOUN
ajst-25954	124	4	is	be	AUX
ajst-25954	124	5	a	a	DET
ajst-25954	124	6	structure	structure	NOUN
ajst-25954	124	7	that	that	PRON
ajst-25954	124	8	applies	apply	VERB
ajst-25954	124	9	multiple	multiple	ADJ
ajst-25954	124	10	different	different	ADJ
ajst-25954	124	11	size	size	NOUN
ajst-25954	124	12	convolutional	convolutional	ADJ
ajst-25954	124	13	kernels	kernel	NOUN
ajst-25954	124	14	in	in	ADP
ajst-25954	124	15	parallel	parallel	NOUN
ajst-25954	124	16	on	on	ADP
ajst-25954	124	17	the	the	DET
ajst-25954	124	18	same	same	ADJ
ajst-25954	124	19	network	network	NOUN
ajst-25954	124	20	layer	layer	NOUN
ajst-25954	124	21	.	.	PUNCT
ajst-25954	125	1	it	it	PRON
ajst-25954	125	2	allows	allow	VERB
ajst-25954	125	3	the	the	DET
ajst-25954	125	4	network	network	NOUN
ajst-25954	125	5	to	to	PART
ajst-25954	125	6	capture	capture	VERB
ajst-25954	125	7	multi	multi	ADJ
ajst-25954	125	8	-	-	ADJ
ajst-25954	125	9	scale	scale	ADJ
ajst-25954	125	10	features	feature	NOUN
ajst-25954	125	11	at	at	ADP
ajst-25954	125	12	a	a	DET
ajst-25954	125	13	single	single	ADJ
ajst-25954	125	14	tier	tier	NOUN
ajst-25954	125	15	.	.	PUNCT
ajst-25954	126	1	by	by	ADP
ajst-25954	126	2	adding	add	VERB
ajst-25954	126	3	the	the	DET
ajst-25954	126	4	eca	eca	NOUN
ajst-25954	126	5	module	module	NOUN
ajst-25954	126	6	to	to	ADP
ajst-25954	126	7	the	the	DET
ajst-25954	126	8	multi	multi	ADJ
ajst-25954	126	9	-	-	ADJ
ajst-25954	126	10	scale	scale	ADJ
ajst-25954	126	11	inception	inception	NOUN
ajst-25954	126	12	module	module	NOUN
ajst-25954	126	13	,	,	PUNCT
ajst-25954	126	14	which	which	PRON
ajst-25954	126	15	can	can	AUX
ajst-25954	126	16	help	help	VERB
ajst-25954	126	17	the	the	DET
ajst-25954	126	18	model	model	NOUN
ajst-25954	126	19	better	well	ADV
ajst-25954	126	20	capture	capture	VERB
ajst-25954	126	21	global	global	ADJ
ajst-25954	126	22	context	context	NOUN
ajst-25954	126	23	information	information	NOUN
ajst-25954	126	24	while	while	SCONJ
ajst-25954	126	25	avoiding	avoid	VERB
ajst-25954	126	26	reducing	reduce	VERB
ajst-25954	126	27	the	the	DET
ajst-25954	126	28	dimensions	dimension	NOUN
ajst-25954	126	29	of	of	ADP
ajst-25954	126	30	the	the	DET
ajst-25954	126	31	model	model	NOUN
ajst-25954	126	32	.	.	PUNCT
ajst-25954	127	1	extracting	extract	VERB
ajst-25954	127	2	spatiotemporal	spatiotemporal	ADJ
ajst-25954	127	3	local	local	ADJ
ajst-25954	127	4	features	feature	NOUN
ajst-25954	127	5	through	through	ADP
ajst-25954	127	6	multi	multi	ADJ
ajst-25954	127	7	-	-	ADJ
ajst-25954	127	8	scale	scale	ADJ
ajst-25954	127	9	inception	inception	NOUN
ajst-25954	127	10	modules	module	NOUN
ajst-25954	127	11	,	,	PUNCT
ajst-25954	127	12	and	and	CCONJ
ajst-25954	127	13	utilizing	utilize	VERB
ajst-25954	127	14	eca	eca	NOUN
ajst-25954	127	15	to	to	PART
ajst-25954	127	16	capture	capture	VERB
ajst-25954	127	17	global	global	ADJ
ajst-25954	127	18	context	context	NOUN
ajst-25954	127	19	information	information	NOUN
ajst-25954	127	20	,	,	PUNCT
ajst-25954	127	21	it	it	PRON
ajst-25954	127	22	can	can	AUX
ajst-25954	127	23	make	make	VERB
ajst-25954	127	24	the	the	DET
ajst-25954	127	25	model	model	NOUN
ajst-25954	127	26	understand	understand	VERB
ajst-25954	127	27	the	the	DET
ajst-25954	127	28	input	input	NOUN
ajst-25954	127	29	data	datum	NOUN
ajst-25954	127	30	more	more	ADV
ajst-25954	127	31	comprehensively	comprehensively	ADV
ajst-25954	127	32	,	,	PUNCT
ajst-25954	127	33	thus	thus	ADV
ajst-25954	127	34	improving	improve	VERB
ajst-25954	127	35	the	the	DET
ajst-25954	127	36	learning	learning	NOUN
ajst-25954	127	37	ability	ability	NOUN
ajst-25954	127	38	and	and	CCONJ
ajst-25954	127	39	performance	performance	NOUN
ajst-25954	127	40	of	of	ADP
ajst-25954	127	41	the	the	DET
ajst-25954	127	42	model	model	NOUN
ajst-25954	127	43	.	.	PUNCT
ajst-25954	128	1	3	3	X
ajst-25954	128	2	.	.	X
ajst-25954	128	3	experimental	experimental	ADJ
ajst-25954	128	4	data	datum	NOUN
ajst-25954	128	5	and	and	CCONJ
ajst-25954	128	6	settings	setting	NOUN
ajst-25954	128	7	1	1	NUM
ajst-25954	128	8	.	.	PUNCT
ajst-25954	129	1	dataset	dataset	NOUN
ajst-25954	129	2	(	(	PUNCT
ajst-25954	129	3	1	1	NUM
ajst-25954	129	4	)	)	PUNCT
ajst-25954	129	5	chb	chb	NOUN
ajst-25954	129	6	-	-	PUNCT
ajst-25954	129	7	mit	mit	NOUN
ajst-25954	129	8	datasets	dataset	NOUN
ajst-25954	129	9	in	in	ADP
ajst-25954	129	10	this	this	DET
ajst-25954	129	11	paper	paper	NOUN
ajst-25954	129	12	,	,	PUNCT
ajst-25954	129	13	the	the	DET
ajst-25954	129	14	proposed	propose	VERB
ajst-25954	129	15	method	method	NOUN
ajst-25954	129	16	is	be	AUX
ajst-25954	129	17	evaluated	evaluate	VERB
ajst-25954	129	18	on	on	ADP
ajst-25954	129	19	the	the	DET
ajst-25954	129	20	chb	chb	NOUN
ajst-25954	129	21	-	-	PUNCT
ajst-25954	129	22	mit	mit	NOUN
ajst-25954	129	23	dataset	dataset	NOUN
ajst-25954	129	24	.	.	PUNCT
ajst-25954	130	1	it	it	PRON
ajst-25954	130	2	consists	consist	VERB
ajst-25954	130	3	of	of	ADP
ajst-25954	130	4	scalp	scalp	NOUN
ajst-25954	130	5	eeg	eeg	NOUN
ajst-25954	130	6	records	record	NOUN
ajst-25954	130	7	taken	take	VERB
ajst-25954	130	8	from	from	ADP
ajst-25954	130	9	children	child	NOUN
ajst-25954	130	10	with	with	ADP
ajst-25954	130	11	epilepsy	epilepsy	NOUN
ajst-25954	130	12	at	at	ADP
ajst-25954	130	13	boston	boston	PROPN
ajst-25954	130	14	children	child	NOUN
ajst-25954	130	15	's	's	PART
ajst-25954	130	16	hospital	hospital	NOUN
ajst-25954	130	17	.	.	PUNCT
ajst-25954	131	1	it	it	PRON
ajst-25954	131	2	contains	contain	VERB
ajst-25954	131	3	23	23	NUM
ajst-25954	131	4	records	record	NOUN
ajst-25954	131	5	from	from	ADP
ajst-25954	131	6	22	22	NUM
ajst-25954	131	7	subjects	subject	NOUN
ajst-25954	131	8	at	at	ADP
ajst-25954	131	9	a	a	DET
ajst-25954	131	10	sampling	sample	VERB
ajst-25954	131	11	frequency	frequency	NOUN
ajst-25954	131	12	of	of	ADP
ajst-25954	131	13	256hz	256hz	PROPN
ajst-25954	131	14	.	.	PUNCT
ajst-25954	132	1	among	among	ADP
ajst-25954	132	2	them	they	PRON
ajst-25954	132	3	,	,	PUNCT
ajst-25954	132	4	each	each	DET
ajst-25954	132	5	patient	patient	NOUN
ajst-25954	132	6	has	have	VERB
ajst-25954	132	7	at	at	ADV
ajst-25954	132	8	least	least	ADV
ajst-25954	132	9	two	two	NUM
ajst-25954	132	10	seizures	seizure	NOUN
ajst-25954	132	11	and	and	CCONJ
ajst-25954	132	12	a	a	DET
ajst-25954	132	13	three	three	NUM
ajst-25954	132	14	-	-	PUNCT
ajst-25954	132	15	hour	hour	NOUN
ajst-25954	132	16	interictal	interictal	ADJ
ajst-25954	132	17	period	period	NOUN
ajst-25954	132	18	recorded	record	VERB
ajst-25954	132	19	,	,	PUNCT
ajst-25954	132	20	and	and	CCONJ
ajst-25954	132	21	these	these	DET
ajst-25954	132	22	patients	patient	NOUN
ajst-25954	132	23	are	be	AUX
ajst-25954	132	24	used	use	VERB
ajst-25954	132	25	for	for	ADP
ajst-25954	132	26	specific	specific	ADJ
ajst-25954	132	27	evaluation	evaluation	NOUN
ajst-25954	132	28	of	of	ADP
ajst-25954	132	29	seizure	seizure	NOUN
ajst-25954	132	30	prediction	prediction	NOUN
ajst-25954	132	31	model	model	NOUN
ajst-25954	132	32	inception	inception	PROPN
ajst-25954	132	33	  	  	SPACE
ajst-25954	132	34	flatte	flatte	PROPN
ajst-25954	132	35	n	n	CCONJ
ajst-25954	132	36	 	 	SPACE
ajst-25954	132	37	la	la	ADV
ajst-25954	132	38	ye	ye	NOUN
ajst-25954	132	39	r	r	NOUN
ajst-25954	132	40	1×1×c	1×1×c	NUM
ajst-25954	132	41	1×1×c	1×1×c	NUM
ajst-25954	132	42	x	x	SYM
ajst-25954	132	43	9	9	NUM
ajst-25954	132	44	9	9	NUM
ajst-25954	132	45	c	c	NOUN
ajst-25954	132	46	x	x	SYM
ajst-25954	132	47	9	9	NUM
ajst-25954	132	48	9	9	NUM
ajst-25954	132	49	c	c	NOUN
ajst-25954	132	50	x’eca	x’eca	PROPN
ajst-25954	133	1	ms‐feature	ms‐feature	PROPN
ajst-25954	133	2	 	 	SPACE
ajst-25954	133	3	conv	conv	PROPN
ajst-25954	133	4	fm	fm	PROPN
ajst-25954	133	5	 	 	SPACE
ajst-25954	133	6	la	la	ADV
ajst-25954	134	1	ye	ye	NOUN
ajst-25954	134	2	r	r	NOUN
ajst-25954	134	3	m	m	VERB
ajst-25954	134	4	lp	lp	NOUN
ajst-25954	134	5	 	 	SPACE
ajst-25954	134	6	lay	lie	VERB
ajst-25954	134	7	e	e	NOUN
ajst-25954	134	8	r	r	NOUN
ajst-25954	134	9	preictal	preictal	ADJ
ajst-25954	134	10	interictal	interictal	ADJ
ajst-25954	134	11	pcfwnet	pcfwnet	NOUN
ajst-25954	134	12	k=5	k=5	PUNCT
ajst-25954	134	13	gap	gap	NOUN
ajst-25954	134	14	σ	σ	PROPN
ajst-25954	134	15	  	  	SPACE
ajst-25954	134	16	softmax	softmax	NOUN
ajst-25954	134	17	figure	figure	NOUN
ajst-25954	134	18	4	4	NUM
ajst-25954	134	19	.	.	PUNCT
ajst-25954	135	1	parallel	parallel	ADJ
ajst-25954	135	2	channel	channel	NOUN
ajst-25954	135	3	feature	feature	NOUN
ajst-25954	135	4	weighted	weight	VERB
ajst-25954	135	5	network	network	NOUN
ajst-25954	135	6	(	(	PUNCT
ajst-25954	135	7	pcfwnet	pcfwnet	NOUN
ajst-25954	135	8	)	)	PUNCT
ajst-25954	136	1	[	[	X
ajst-25954	136	2	27	27	NUM
ajst-25954	136	3	]	]	PUNCT
ajst-25954	136	4	.	.	PUNCT
ajst-25954	137	1	in	in	ADP
ajst-25954	137	2	addition	addition	NOUN
ajst-25954	137	3	,	,	PUNCT
ajst-25954	137	4	the	the	DET
ajst-25954	137	5	study	study	NOUN
ajst-25954	137	6	excluded	exclude	VERB
ajst-25954	137	7	records	record	NOUN
ajst-25954	137	8	of	of	ADP
ajst-25954	137	9	multiple	multiple	ADJ
ajst-25954	137	10	seizures	seizure	NOUN
ajst-25954	137	11	occurring	occur	VERB
ajst-25954	137	12	within	within	ADP
ajst-25954	137	13	two	two	NUM
ajst-25954	137	14	hours	hour	NOUN
ajst-25954	137	15	.	.	PUNCT
ajst-25954	138	1	to	to	PART
ajst-25954	138	2	rule	rule	VERB
ajst-25954	138	3	out	out	ADP
ajst-25954	138	4	the	the	DET
ajst-25954	138	5	effect	effect	NOUN
ajst-25954	138	6	on	on	ADP
ajst-25954	138	7	later	late	ADJ
ajst-25954	138	8	seizures	seizure	NOUN
ajst-25954	138	9	and	and	CCONJ
ajst-25954	138	10	help	help	VERB
ajst-25954	138	11	the	the	DET
ajst-25954	138	12	model	model	NOUN
ajst-25954	138	13	predict	predict	VERB
ajst-25954	138	14	major	major	ADJ
ajst-25954	138	15	seizures	seizure	NOUN
ajst-25954	138	16	[	[	X
ajst-25954	138	17	28	28	NUM
ajst-25954	138	18	]	]	PUNCT
ajst-25954	139	1	.	.	PUNCT
ajst-25954	140	1	(	(	PUNCT
ajst-25954	140	2	2	2	X
ajst-25954	140	3	)	)	PUNCT
ajst-25954	140	4	bonn	bonn	PROPN
ajst-25954	140	5	datasets	dataset	NOUN
ajst-25954	140	6	the	the	DET
ajst-25954	140	7	bonn	bonn	PROPN
ajst-25954	140	8	dataset	dataset	NOUN
ajst-25954	140	9	consists	consist	VERB
ajst-25954	140	10	eeg	eeg	NOUN
ajst-25954	140	11	data	datum	NOUN
ajst-25954	140	12	from	from	ADP
ajst-25954	140	13	5	5	NUM
ajst-25954	140	14	healthy	healthy	ADJ
ajst-25954	140	15	people	people	NOUN
ajst-25954	140	16	and	and	CCONJ
ajst-25954	140	17	5	5	NUM
ajst-25954	140	18	patients	patient	NOUN
ajst-25954	140	19	with	with	ADP
ajst-25954	140	20	epilepsy	epilepsy	NOUN
ajst-25954	140	21	,	,	PUNCT
ajst-25954	140	22	which	which	PRON
ajst-25954	140	23	contains	contain	VERB
ajst-25954	140	24	5	5	NUM
ajst-25954	140	25	data	datum	NOUN
ajst-25954	140	26	subsets	subset	NOUN
ajst-25954	140	27	,	,	PUNCT
ajst-25954	140	28	namely	namely	ADV
ajst-25954	140	29	f	f	NUM
ajst-25954	140	30	,	,	PUNCT
ajst-25954	140	31	s	s	PROPN
ajst-25954	140	32	,	,	PUNCT
ajst-25954	140	33	n	n	CCONJ
ajst-25954	140	34	,	,	PUNCT
ajst-25954	140	35	z	z	NOUN
ajst-25954	140	36	,	,	PUNCT
ajst-25954	140	37	and	and	CCONJ
ajst-25954	140	38	o.	o.	NOUN
ajst-25954	140	39	each	each	PRON
ajst-25954	140	40	of	of	ADP
ajst-25954	140	41	these	these	DET
ajst-25954	140	42	sub	sub	NOUN
ajst-25954	140	43	-	-	NOUN
ajst-25954	140	44	datasets	dataset	NOUN
ajst-25954	140	45	contains	contain	VERB
ajst-25954	140	46	100	100	NUM
ajst-25954	140	47	data	datum	NOUN
ajst-25954	140	48	fragments	fragment	NOUN
ajst-25954	140	49	,	,	PUNCT
ajst-25954	140	50	each	each	PRON
ajst-25954	140	51	with	with	ADP
ajst-25954	140	52	a	a	DET
ajst-25954	140	53	duration	duration	NOUN
ajst-25954	140	54	of	of	ADP
ajst-25954	140	55	23.6	23.6	NUM
ajst-25954	140	56	seconds	second	NOUN
ajst-25954	140	57	.	.	PUNCT
ajst-25954	141	1	as	as	SCONJ
ajst-25954	141	2	can	can	AUX
ajst-25954	141	3	be	be	AUX
ajst-25954	141	4	seen	see	VERB
ajst-25954	141	5	from	from	ADP
ajst-25954	141	6	the	the	DET
ajst-25954	141	7	table	table	NOUN
ajst-25954	141	8	i.	i.	PROPN
ajst-25954	141	9	data	data	PROPN
ajst-25954	141	10	z	z	PROPN
ajst-25954	141	11	and	and	CCONJ
ajst-25954	141	12	o	o	PROPN
ajst-25954	141	13	are	be	AUX
ajst-25954	141	14	scalp	scalp	NOUN
ajst-25954	141	15	eeg	eeg	NOUN
ajst-25954	141	16	information	information	NOUN
ajst-25954	141	17	of	of	ADP
ajst-25954	141	18	5	5	NUM
ajst-25954	141	19	healthy	healthy	ADJ
ajst-25954	141	20	people	people	NOUN
ajst-25954	141	21	,	,	PUNCT
ajst-25954	141	22	which	which	PRON
ajst-25954	141	23	constituted	constitute	VERB
ajst-25954	141	24	the	the	DET
ajst-25954	141	25	comparison	comparison	NOUN
ajst-25954	141	26	group	group	NOUN
ajst-25954	141	27	.	.	PUNCT
ajst-25954	142	1	the	the	DET
ajst-25954	142	2	fragment	fragment	NOUN
ajst-25954	142	3	in	in	ADP
ajst-25954	142	4	z	z	PROPN
ajst-25954	142	5	is	be	AUX
ajst-25954	142	6	eeg	eeg	NOUN
ajst-25954	142	7	when	when	SCONJ
ajst-25954	142	8	the	the	DET
ajst-25954	142	9	subject	subject	NOUN
ajst-25954	142	10	's	's	PART
ajst-25954	142	11	eyes	eye	NOUN
ajst-25954	142	12	are	be	AUX
ajst-25954	142	13	open	open	ADJ
ajst-25954	142	14	,	,	PUNCT
ajst-25954	142	15	and	and	CCONJ
ajst-25954	142	16	the	the	DET
ajst-25954	142	17	fragment	fragment	NOUN
ajst-25954	142	18	in	in	ADP
ajst-25954	142	19	o	o	PROPN
ajst-25954	142	20	is	be	AUX
ajst-25954	142	21	eeg	eeg	NOUN
ajst-25954	142	22	when	when	SCONJ
ajst-25954	142	23	86	86	NUM
ajst-25954	142	24	the	the	DET
ajst-25954	142	25	subject	subject	NOUN
ajst-25954	142	26	's	's	PART
ajst-25954	142	27	eyes	eye	NOUN
ajst-25954	142	28	are	be	AUX
ajst-25954	142	29	closed	close	VERB
ajst-25954	142	30	.	.	PUNCT
ajst-25954	143	1	data	datum	NOUN
ajst-25954	143	2	n	n	CCONJ
ajst-25954	143	3	,	,	PUNCT
ajst-25954	143	4	f	f	PROPN
ajst-25954	143	5	and	and	CCONJ
ajst-25954	143	6	s	s	VERB
ajst-25954	143	7	are	be	AUX
ajst-25954	143	8	intracranial	intracranial	ADJ
ajst-25954	143	9	eeg	eeg	NOUN
ajst-25954	143	10	collected	collect	VERB
ajst-25954	143	11	from	from	ADP
ajst-25954	143	12	5	5	NUM
ajst-25954	143	13	patients	patient	NOUN
ajst-25954	143	14	with	with	ADP
ajst-25954	143	15	epilepsy	epilepsy	NOUN
ajst-25954	143	16	.	.	PUNCT
ajst-25954	144	1	n	n	PROPN
ajst-25954	144	2	and	and	CCONJ
ajst-25954	144	3	f	f	PROPN
ajst-25954	144	4	are	be	AUX
ajst-25954	144	5	collected	collect	VERB
ajst-25954	144	6	during	during	ADP
ajst-25954	144	7	the	the	DET
ajst-25954	144	8	interictal	interictal	ADJ
ajst-25954	144	9	period	period	NOUN
ajst-25954	144	10	,	,	PUNCT
ajst-25954	144	11	and	and	CCONJ
ajst-25954	144	12	s	s	VERB
ajst-25954	144	13	is	be	AUX
ajst-25954	144	14	collected	collect	VERB
ajst-25954	144	15	during	during	ADP
ajst-25954	144	16	the	the	DET
ajst-25954	144	17	seizure	seizure	NOUN
ajst-25954	144	18	period	period	NOUN
ajst-25954	144	19	.	.	PUNCT
ajst-25954	145	1	table	table	NOUN
ajst-25954	145	2	i.	i.	PROPN
ajst-25954	145	3	bonn	bonn	PROPN
ajst-25954	145	4	dataset	dataset	PROPN
ajst-25954	145	5	introduction	introduction	NOUN
ajst-25954	145	6	dataset	dataset	NOUN
ajst-25954	145	7	status	status	NOUN
ajst-25954	145	8	evaluate	evaluate	VERB
ajst-25954	145	9	state	state	NOUN
ajst-25954	145	10	record	record	NOUN
ajst-25954	145	11	resources	resource	NOUN
ajst-25954	145	12	a_z	a_z	NOUN
ajst-25954	145	13	normal	normal	ADJ
ajst-25954	145	14	eye	eye	NOUN
ajst-25954	145	15	open	open	ADJ
ajst-25954	145	16	surface	surface	NOUN
ajst-25954	145	17	electrode	electrode	NOUN
ajst-25954	145	18	b_o	b_o	SYM
ajst-25954	145	19	normal	normal	ADJ
ajst-25954	145	20	eye	eye	NOUN
ajst-25954	145	21	closed	close	VERB
ajst-25954	145	22	surface	surface	NOUN
ajst-25954	145	23	electrode	electrode	NOUN
ajst-25954	145	24	c_f	c_f	PROPN
ajst-25954	145	25	patient	patient	ADJ
ajst-25954	145	26	interictal	interictal	ADJ
ajst-25954	145	27	inner	inner	ADJ
ajst-25954	145	28	electrode	electrode	NOUN
ajst-25954	145	29	d_f	d_f	ADP
ajst-25954	145	30	patient	patient	ADJ
ajst-25954	145	31	interictal	interictal	ADJ
ajst-25954	145	32	inner	inner	ADJ
ajst-25954	145	33	electrode	electrode	NOUN
ajst-25954	145	34	e_s	e_s	PUNCT
ajst-25954	145	35	patient	patient	NOUN
ajst-25954	145	36	seizure	seizure	VERB
ajst-25954	145	37	inner	inner	ADJ
ajst-25954	145	38	electrode	electrode	NOUN
ajst-25954	145	39	2	2	NUM
ajst-25954	145	40	.	.	PUNCT
ajst-25954	145	41	experimental	experimental	ADJ
ajst-25954	145	42	setting	setting	NOUN
ajst-25954	145	43	and	and	CCONJ
ajst-25954	145	44	evaluation	evaluation	NOUN
ajst-25954	145	45	indicators	indicator	NOUN
ajst-25954	145	46	this	this	DET
ajst-25954	145	47	paper	paper	NOUN
ajst-25954	145	48	is	be	AUX
ajst-25954	145	49	based	base	VERB
ajst-25954	145	50	on	on	ADP
ajst-25954	145	51	the	the	DET
ajst-25954	145	52	chb	chb	NOUN
ajst-25954	145	53	-	-	PUNCT
ajst-25954	145	54	mit	mit	NOUN
ajst-25954	145	55	and	and	CCONJ
ajst-25954	145	56	bonn	bonn	PROPN
ajst-25954	145	57	public	public	ADJ
ajst-25954	145	58	epilepsy	epilepsy	NOUN
ajst-25954	145	59	datasets	dataset	NOUN
ajst-25954	145	60	.	.	PUNCT
ajst-25954	146	1	in	in	ADP
ajst-25954	146	2	recent	recent	ADJ
ajst-25954	146	3	studies	study	NOUN
ajst-25954	146	4	,	,	PUNCT
ajst-25954	146	5	preictal	preictal	ADJ
ajst-25954	146	6	period	period	NOUN
ajst-25954	146	7	is	be	AUX
ajst-25954	146	8	generally	generally	ADV
ajst-25954	146	9	considered	consider	VERB
ajst-25954	146	10	to	to	PART
ajst-25954	146	11	be	be	AUX
ajst-25954	146	12	30	30	NUM
ajst-25954	146	13	minutes	minute	NOUN
ajst-25954	146	14	or	or	CCONJ
ajst-25954	146	15	15	15	NUM
ajst-25954	146	16	minutes	minute	NOUN
ajst-25954	146	17	before	before	ADP
ajst-25954	146	18	onset	onset	NOUN
ajst-25954	146	19	[	[	X
ajst-25954	146	20	29	29	NUM
ajst-25954	146	21	]	]	PUNCT
ajst-25954	146	22	,	,	PUNCT
ajst-25954	146	23	which	which	DET
ajst-25954	146	24	interictal	interictal	ADJ
ajst-25954	146	25	period	period	NOUN
ajst-25954	146	26	is	be	AUX
ajst-25954	146	27	defined	define	VERB
ajst-25954	146	28	as	as	ADP
ajst-25954	146	29	the	the	DET
ajst-25954	146	30	time	time	NOUN
ajst-25954	146	31	before	before	ADV
ajst-25954	146	32	and	and	CCONJ
ajst-25954	146	33	at	at	ADV
ajst-25954	146	34	least	least	ADV
ajst-25954	146	35	2	2	NUM
ajst-25954	146	36	hours	hour	NOUN
ajst-25954	146	37	after	after	SCONJ
ajst-25954	146	38	the	the	DET
ajst-25954	146	39	seizure	seizure	NOUN
ajst-25954	146	40	ended	end	VERB
ajst-25954	146	41	[	[	X
ajst-25954	146	42	30	30	NUM
ajst-25954	146	43	]	]	PUNCT
ajst-25954	146	44	.	.	PUNCT
ajst-25954	147	1	therefore	therefore	ADV
ajst-25954	147	2	,	,	PUNCT
ajst-25954	147	3	the	the	DET
ajst-25954	147	4	same	same	ADJ
ajst-25954	147	5	preictal	preictal	ADJ
ajst-25954	147	6	and	and	CCONJ
ajst-25954	147	7	interictal	interictal	ADJ
ajst-25954	147	8	time	time	NOUN
ajst-25954	147	9	settings	setting	NOUN
ajst-25954	147	10	are	be	AUX
ajst-25954	147	11	used	use	VERB
ajst-25954	147	12	in	in	ADP
ajst-25954	147	13	this	this	DET
ajst-25954	147	14	study	study	NOUN
ajst-25954	147	15	.	.	PUNCT
ajst-25954	148	1	the	the	DET
ajst-25954	148	2	dataset	dataset	NOUN
ajst-25954	148	3	is	be	AUX
ajst-25954	148	4	clipped	clip	VERB
ajst-25954	148	5	as	as	ADP
ajst-25954	148	6	a	a	DET
ajst-25954	148	7	time	time	NOUN
ajst-25954	148	8	window	window	NOUN
ajst-25954	148	9	lasting	last	VERB
ajst-25954	148	10	4	4	NUM
ajst-25954	148	11	seconds	second	NOUN
ajst-25954	148	12	before	before	SCONJ
ajst-25954	148	13	it	it	PRON
ajst-25954	148	14	is	be	AUX
ajst-25954	148	15	input	input	NOUN
ajst-25954	148	16	into	into	ADP
ajst-25954	148	17	ms	ms	PROPN
ajst-25954	148	18	-	-	PUNCT
ajst-25954	148	19	stfm	stfm	NOUN
ajst-25954	148	20	-	-	PUNCT
ajst-25954	148	21	pcfwnet	pcfwnet	NOUN
ajst-25954	148	22	.	.	PUNCT
ajst-25954	149	1	the	the	DET
ajst-25954	149	2	continuous	continuous	ADJ
ajst-25954	149	3	eeg	eeg	NOUN
ajst-25954	149	4	signals	signal	NOUN
ajst-25954	149	5	15	15	NUM
ajst-25954	149	6	to	to	PART
ajst-25954	149	7	30	30	NUM
ajst-25954	149	8	minutes	minute	NOUN
ajst-25954	149	9	before	before	SCONJ
ajst-25954	149	10	the	the	DET
ajst-25954	149	11	seizure	seizure	NOUN
ajst-25954	149	12	are	be	AUX
ajst-25954	149	13	considered	consider	VERB
ajst-25954	149	14	as	as	ADP
ajst-25954	149	15	preictal	preictal	ADJ
ajst-25954	149	16	period	period	NOUN
ajst-25954	149	17	.	.	PUNCT
ajst-25954	150	1	the	the	DET
ajst-25954	150	2	time	time	NOUN
ajst-25954	150	3	between	between	ADP
ajst-25954	150	4	the	the	DET
ajst-25954	150	5	end	end	NOUN
ajst-25954	150	6	of	of	ADP
ajst-25954	150	7	an	an	DET
ajst-25954	150	8	onset	onset	NOUN
ajst-25954	150	9	and	and	CCONJ
ajst-25954	150	10	the	the	DET
ajst-25954	150	11	start	start	NOUN
ajst-25954	150	12	of	of	ADP
ajst-25954	150	13	the	the	DET
ajst-25954	150	14	next	next	ADJ
ajst-25954	150	15	onset	onset	NOUN
ajst-25954	150	16	is	be	AUX
ajst-25954	150	17	defined	define	VERB
ajst-25954	150	18	as	as	ADP
ajst-25954	150	19	the	the	DET
ajst-25954	150	20	interictal	interictal	ADJ
ajst-25954	150	21	period	period	NOUN
ajst-25954	150	22	.	.	PUNCT
ajst-25954	151	1	eeg	eeg	NOUN
ajst-25954	151	2	features	feature	NOUN
ajst-25954	151	3	are	be	AUX
ajst-25954	151	4	extracted	extract	VERB
ajst-25954	151	5	from	from	ADP
ajst-25954	151	6	the	the	DET
ajst-25954	151	7	chb	chb	NOUN
ajst-25954	151	8	-	-	PUNCT
ajst-25954	151	9	mit	mit	NOUN
ajst-25954	151	10	dataset	dataset	NOUN
ajst-25954	151	11	,	,	PUNCT
ajst-25954	151	12	which	which	PRON
ajst-25954	151	13	the	the	DET
ajst-25954	151	14	pre	pre	NOUN
ajst-25954	151	15	-	-	ADJ
ajst-25954	151	16	seizure	seizure	ADJ
ajst-25954	151	17	duration	duration	NOUN
ajst-25954	151	18	of	of	ADP
ajst-25954	151	19	each	each	DET
ajst-25954	151	20	seizure	seizure	NOUN
ajst-25954	151	21	recorded	record	VERB
ajst-25954	151	22	in	in	ADP
ajst-25954	151	23	the	the	DET
ajst-25954	151	24	dataset	dataset	NOUN
ajst-25954	151	25	is	be	AUX
ajst-25954	151	26	defined	define	VERB
ajst-25954	151	27	as	as	ADP
ajst-25954	151	28	1800	1800	NUM
ajst-25954	151	29	seconds	second	NOUN
ajst-25954	151	30	,	,	PUNCT
ajst-25954	151	31	and	and	CCONJ
ajst-25954	151	32	the	the	DET
ajst-25954	151	33	inter	inter	NOUN
ajst-25954	151	34	-	-	ADJ
ajst-25954	151	35	seizure	seizure	ADJ
ajst-25954	151	36	duration	duration	NOUN
ajst-25954	151	37	as	as	ADP
ajst-25954	151	38	1200	1200	NUM
ajst-25954	151	39	seconds	second	NOUN
ajst-25954	151	40	.	.	PUNCT
ajst-25954	152	1	setting	set	VERB
ajst-25954	152	2	to	to	ADP
ajst-25954	152	3	one	one	NUM
ajst-25954	152	4	sample	sample	NOUN
ajst-25954	152	5	every	every	DET
ajst-25954	152	6	4	4	NUM
ajst-25954	152	7	seconds	second	NOUN
ajst-25954	152	8	,	,	PUNCT
ajst-25954	152	9	each	each	DET
ajst-25954	152	10	pre	pre	NOUN
ajst-25954	152	11	-	-	VERB
ajst-25954	152	12	seizure	seizure	ADJ
ajst-25954	152	13	and	and	CCONJ
ajst-25954	152	14	inter	inter	ADJ
ajst-25954	152	15	-	-	ADJ
ajst-25954	152	16	seizure	seizure	ADJ
ajst-25954	152	17	period	period	NOUN
ajst-25954	152	18	can	can	AUX
ajst-25954	152	19	obtain	obtain	VERB
ajst-25954	152	20	450	450	NUM
ajst-25954	152	21	and	and	CCONJ
ajst-25954	152	22	300	300	NUM
ajst-25954	152	23	samples	sample	NOUN
ajst-25954	152	24	,	,	PUNCT
ajst-25954	152	25	respectively	respectively	ADV
ajst-25954	152	26	.	.	PUNCT
ajst-25954	153	1	the	the	DET
ajst-25954	153	2	spatiotemporal	spatiotemporal	ADJ
ajst-25954	153	3	features	feature	NOUN
ajst-25954	153	4	of	of	ADP
ajst-25954	153	5	18	18	NUM
ajst-25954	153	6	channels	channel	NOUN
ajst-25954	153	7	eeg	eeg	NOUN
ajst-25954	153	8	signals	signal	NOUN
ajst-25954	153	9	are	be	AUX
ajst-25954	153	10	extracted	extract	VERB
ajst-25954	153	11	from	from	ADP
ajst-25954	153	12	each	each	DET
ajst-25954	153	13	sample	sample	NOUN
ajst-25954	153	14	.	.	PUNCT
ajst-25954	154	1	each	each	DET
ajst-25954	154	2	bonn	bonn	PROPN
ajst-25954	154	3	data	data	PROPN
ajst-25954	154	4	subset	subset	NOUN
ajst-25954	154	5	contains	contain	VERB
ajst-25954	154	6	100	100	NUM
ajst-25954	154	7	single	single	ADJ
ajst-25954	154	8	channel	channel	NOUN
ajst-25954	154	9	i	i	PROPN
ajst-25954	154	10	-	-	PUNCT
ajst-25954	154	11	eeg	eeg	PROPN
ajst-25954	154	12	records	record	NOUN
ajst-25954	154	13	,	,	PUNCT
ajst-25954	154	14	for	for	ADP
ajst-25954	154	15	a	a	DET
ajst-25954	154	16	total	total	NOUN
ajst-25954	154	17	of	of	ADP
ajst-25954	154	18	500	500	NUM
ajst-25954	154	19	records	record	NOUN
ajst-25954	154	20	.	.	PUNCT
ajst-25954	155	1	the	the	DET
ajst-25954	155	2	time	time	NOUN
ajst-25954	155	3	length	length	NOUN
ajst-25954	155	4	of	of	ADP
ajst-25954	155	5	each	each	DET
ajst-25954	155	6	data	datum	NOUN
ajst-25954	155	7	fragment	fragment	NOUN
ajst-25954	155	8	in	in	ADP
ajst-25954	155	9	the	the	DET
ajst-25954	155	10	database	database	NOUN
ajst-25954	155	11	is	be	AUX
ajst-25954	155	12	23.6	23.6	NUM
ajst-25954	155	13	seconds	second	NOUN
ajst-25954	155	14	and	and	CCONJ
ajst-25954	155	15	the	the	DET
ajst-25954	155	16	sampling	sample	VERB
ajst-25954	155	17	frequency	frequency	NOUN
ajst-25954	155	18	is	be	AUX
ajst-25954	155	19	173.61hz	173.61hz	NUM
ajst-25954	155	20	.	.	PUNCT
ajst-25954	156	1	a	a	DET
ajst-25954	156	2	total	total	NOUN
ajst-25954	156	3	of	of	ADP
ajst-25954	156	4	4097	4097	NUM
ajst-25954	156	5	samples	sample	NOUN
ajst-25954	156	6	are	be	AUX
ajst-25954	156	7	recorded	record	VERB
ajst-25954	156	8	in	in	ADP
ajst-25954	156	9	each	each	DET
ajst-25954	156	10	data	datum	NOUN
ajst-25954	156	11	subset	subset	VERB
ajst-25954	156	12	.	.	PUNCT
ajst-25954	157	1	in	in	ADP
ajst-25954	157	2	this	this	DET
ajst-25954	157	3	paper	paper	NOUN
ajst-25954	157	4	,	,	PUNCT
ajst-25954	157	5	the	the	DET
ajst-25954	157	6	bonn	bonn	PROPN
ajst-25954	157	7	dataset	dataset	NOUN
ajst-25954	157	8	is	be	AUX
ajst-25954	157	9	divided	divide	VERB
ajst-25954	157	10	into	into	ADP
ajst-25954	157	11	five	five	NUM
ajst-25954	157	12	non	non	ADJ
ajst-25954	157	13	-	-	ADJ
ajst-25954	157	14	epileptic	epileptic	ADJ
ajst-25954	157	15	and	and	CCONJ
ajst-25954	157	16	epileptic	epileptic	ADJ
ajst-25954	157	17	control	control	NOUN
ajst-25954	157	18	groups	group	NOUN
ajst-25954	157	19	,	,	PUNCT
ajst-25954	157	20	namely	namely	ADV
ajst-25954	157	21	a_e	a_e	SYM
ajst-25954	157	22	,	,	PUNCT
ajst-25954	157	23	b_e	b_e	NUM
ajst-25954	157	24	,	,	PUNCT
ajst-25954	157	25	c_e	c_e	PRON
ajst-25954	157	26	,	,	PUNCT
ajst-25954	157	27	d_e	d_e	PROPN
ajst-25954	157	28	and	and	CCONJ
ajst-25954	157	29	abcd_e	abcd_e	PROPN
ajst-25954	157	30	.	.	PUNCT
ajst-25954	158	1	because	because	SCONJ
ajst-25954	158	2	the	the	DET
ajst-25954	158	3	model	model	NOUN
ajst-25954	158	4	needs	need	VERB
ajst-25954	158	5	to	to	PART
ajst-25954	158	6	deal	deal	VERB
ajst-25954	158	7	with	with	ADP
ajst-25954	158	8	more	more	ADJ
ajst-25954	158	9	dimensions	dimension	NOUN
ajst-25954	158	10	,	,	PUNCT
ajst-25954	158	11	too	too	ADV
ajst-25954	158	12	many	many	ADJ
ajst-25954	158	13	features	feature	NOUN
ajst-25954	158	14	can	can	AUX
ajst-25954	158	15	affect	affect	VERB
ajst-25954	158	16	training	training	NOUN
ajst-25954	158	17	and	and	CCONJ
ajst-25954	158	18	test	test	NOUN
ajst-25954	158	19	results	result	NOUN
ajst-25954	158	20	.	.	PUNCT
ajst-25954	159	1	this	this	PRON
ajst-25954	159	2	may	may	AUX
ajst-25954	159	3	can	can	AUX
ajst-25954	159	4	increase	increase	VERB
ajst-25954	159	5	training	training	NOUN
ajst-25954	159	6	time	time	NOUN
ajst-25954	159	7	and	and	CCONJ
ajst-25954	159	8	increase	increase	VERB
ajst-25954	159	9	the	the	DET
ajst-25954	159	10	risk	risk	NOUN
ajst-25954	159	11	of	of	ADP
ajst-25954	159	12	overfitting	overfitte	VERB
ajst-25954	159	13	the	the	DET
ajst-25954	159	14	model	model	NOUN
ajst-25954	159	15	.	.	PUNCT
ajst-25954	160	1	in	in	ADP
ajst-25954	160	2	addition	addition	NOUN
ajst-25954	160	3	,	,	PUNCT
ajst-25954	160	4	too	too	ADV
ajst-25954	160	5	many	many	ADJ
ajst-25954	160	6	features	feature	NOUN
ajst-25954	160	7	also	also	ADV
ajst-25954	160	8	increase	increase	VERB
ajst-25954	160	9	the	the	DET
ajst-25954	160	10	presence	presence	NOUN
ajst-25954	160	11	of	of	ADP
ajst-25954	160	12	noise	noise	NOUN
ajst-25954	160	13	and	and	CCONJ
ajst-25954	160	14	redundant	redundant	ADJ
ajst-25954	160	15	information	information	NOUN
ajst-25954	160	16	,	,	PUNCT
ajst-25954	160	17	which	which	PRON
ajst-25954	160	18	make	make	VERB
ajst-25954	160	19	it	it	PRON
ajst-25954	160	20	more	more	ADV
ajst-25954	160	21	difficult	difficult	ADJ
ajst-25954	160	22	to	to	PART
ajst-25954	160	23	learn	learn	VERB
ajst-25954	160	24	and	and	CCONJ
ajst-25954	160	25	generalize	generalize	VERB
ajst-25954	160	26	the	the	DET
ajst-25954	160	27	model	model	NOUN
ajst-25954	160	28	.	.	PUNCT
ajst-25954	161	1	to	to	PART
ajst-25954	161	2	avoid	avoid	VERB
ajst-25954	161	3	this	this	DET
ajst-25954	161	4	problem	problem	NOUN
ajst-25954	161	5	,	,	PUNCT
ajst-25954	161	6	feature	feature	NOUN
ajst-25954	161	7	selection	selection	NOUN
ajst-25954	161	8	method	method	NOUN
ajst-25954	161	9	is	be	AUX
ajst-25954	161	10	used	use	VERB
ajst-25954	161	11	to	to	PART
ajst-25954	161	12	select	select	VERB
ajst-25954	161	13	the	the	DET
ajst-25954	161	14	features	feature	NOUN
ajst-25954	161	15	most	most	ADV
ajst-25954	161	16	relevant	relevant	ADJ
ajst-25954	161	17	to	to	ADP
ajst-25954	161	18	the	the	DET
ajst-25954	161	19	seizure	seizure	NOUN
ajst-25954	161	20	prediction	prediction	NOUN
ajst-25954	161	21	task	task	NOUN
ajst-25954	161	22	.	.	PUNCT
ajst-25954	162	1	it	it	PRON
ajst-25954	162	2	includes	include	VERB
ajst-25954	162	3	mean	mean	ADJ
ajst-25954	162	4	,	,	PUNCT
ajst-25954	162	5	standard	standard	ADJ
ajst-25954	162	6	deviation	deviation	NOUN
ajst-25954	162	7	,	,	PUNCT
ajst-25954	162	8	peak	peak	NOUN
ajst-25954	162	9	,	,	PUNCT
ajst-25954	162	10	fuzzy	fuzzy	ADJ
ajst-25954	162	11	entropy	entropy	NOUN
ajst-25954	162	12	,	,	PUNCT
ajst-25954	162	13	skewness	skewness	NOUN
ajst-25954	162	14	and	and	CCONJ
ajst-25954	162	15	variance	variance	NOUN
ajst-25954	162	16	.	.	PUNCT
ajst-25954	163	1	in	in	ADP
ajst-25954	163	2	order	order	NOUN
ajst-25954	163	3	to	to	PART
ajst-25954	163	4	evaluate	evaluate	VERB
ajst-25954	163	5	the	the	DET
ajst-25954	163	6	performance	performance	NOUN
ajst-25954	163	7	of	of	ADP
ajst-25954	163	8	the	the	DET
ajst-25954	163	9	method	method	NOUN
ajst-25954	163	10	,	,	PUNCT
ajst-25954	163	11	the	the	DET
ajst-25954	163	12	5fold	5fold	NUM
ajst-25954	163	13	cross	cross	ADJ
ajst-25954	163	14	-	-	ADJ
ajst-25954	163	15	validation	validation	ADJ
ajst-25954	163	16	technique	technique	NOUN
ajst-25954	163	17	and	and	CCONJ
ajst-25954	163	18	three	three	NUM
ajst-25954	163	19	indexes	index	NOUN
ajst-25954	163	20	,	,	PUNCT
ajst-25954	163	21	including	include	VERB
ajst-25954	163	22	accuracy	accuracy	NOUN
ajst-25954	163	23	,	,	PUNCT
ajst-25954	163	24	sensitivity	sensitivity	NOUN
ajst-25954	163	25	and	and	CCONJ
ajst-25954	163	26	specificity	specificity	NOUN
ajst-25954	163	27	,	,	PUNCT
ajst-25954	163	28	are	be	AUX
ajst-25954	163	29	used	use	VERB
ajst-25954	163	30	.	.	PUNCT
ajst-25954	164	1	considering	consider	VERB
ajst-25954	164	2	that	that	SCONJ
ajst-25954	164	3	choosing	choose	VERB
ajst-25954	164	4	a	a	DET
ajst-25954	164	5	large	large	ADJ
ajst-25954	164	6	number	number	NOUN
ajst-25954	164	7	of	of	ADP
ajst-25954	164	8	iterations	iteration	NOUN
ajst-25954	164	9	can	can	AUX
ajst-25954	164	10	easily	easily	ADV
ajst-25954	164	11	lead	lead	VERB
ajst-25954	164	12	to	to	ADP
ajst-25954	164	13	overfitting	overfitte	VERB
ajst-25954	164	14	,	,	PUNCT
ajst-25954	164	15	so	so	SCONJ
ajst-25954	164	16	this	this	DET
ajst-25954	164	17	paper	paper	NOUN
ajst-25954	164	18	introduces	introduce	VERB
ajst-25954	164	19	an	an	DET
ajst-25954	164	20	adam	adam	PROPN
ajst-25954	164	21	optimizer	optimizer	NOUN
ajst-25954	164	22	with	with	ADP
ajst-25954	164	23	a	a	DET
ajst-25954	164	24	learning	learn	VERB
ajst-25954	164	25	rate	rate	NOUN
ajst-25954	164	26	of	of	ADP
ajst-25954	164	27	0.0001	0.0001	NUM
ajst-25954	164	28	to	to	PART
ajst-25954	164	29	minimize	minimize	VERB
ajst-25954	164	30	the	the	DET
ajst-25954	164	31	loss	loss	NOUN
ajst-25954	164	32	function	function	NOUN
ajst-25954	164	33	.	.	PUNCT
ajst-25954	165	1	table	table	NOUN
ajst-25954	165	2	ii	ii	PROPN
ajst-25954	165	3	.	.	PUNCT
ajst-25954	166	1	the	the	DET
ajst-25954	166	2	proposed	propose	VERB
ajst-25954	166	3	method	method	NOUN
ajst-25954	166	4	results	result	NOUN
ajst-25954	166	5	in	in	ADP
ajst-25954	166	6	data	datum	NOUN
ajst-25954	166	7	on	on	ADP
ajst-25954	166	8	the	the	DET
ajst-25954	166	9	chb	chb	NOUN
ajst-25954	166	10	-	-	PUNCT
ajst-25954	166	11	mit	mit	NOUN
ajst-25954	166	12	dataset	dataset	NOUN
ajst-25954	166	13	p.id	p.id	NOUN
ajst-25954	166	14	used	use	VERB
ajst-25954	166	15	cases	case	NOUN
ajst-25954	166	16	lr	lr	NOUN
ajst-25954	166	17	batch	batch	NOUN
ajst-25954	166	18	size	size	NOUN
ajst-25954	166	19	acc(%	acc(%	NOUN
ajst-25954	166	20	)	)	PUNCT
ajst-25954	166	21	spe(%	spe(%	NOUN
ajst-25954	166	22	)	)	PUNCT
ajst-25954	166	23	sen(%	sen(%	PROPN
ajst-25954	166	24	)	)	PUNCT
ajst-25954	167	1	chb01	chb01	VERB
ajst-25954	167	2	7	7	NUM
ajst-25954	167	3	10	10	NUM
ajst-25954	167	4	-	-	SYM
ajst-25954	167	5	4	4	NUM
ajst-25954	167	6	32	32	NUM
ajst-25954	167	7	100	100	NUM
ajst-25954	167	8	100	100	NUM
ajst-25954	167	9	100	100	NUM
ajst-25954	167	10	chb02	chb02	NOUN
ajst-25954	167	11	3	3	NUM
ajst-25954	167	12	10	10	NUM
ajst-25954	167	13	-	-	SYM
ajst-25954	167	14	4	4	NUM
ajst-25954	167	15	32	32	NUM
ajst-25954	167	16	97.8	97.8	NUM
ajst-25954	167	17	100	100	NUM
ajst-25954	167	18	99.4	99.4	NUM
ajst-25954	167	19	chb03	chb03	NOUN
ajst-25954	167	20	6	6	NUM
ajst-25954	167	21	10	10	NUM
ajst-25954	167	22	-	-	SYM
ajst-25954	167	23	4	4	NUM
ajst-25954	167	24	32	32	NUM
ajst-25954	167	25	95.8	95.8	NUM
ajst-25954	167	26	93.1	93.1	NUM
ajst-25954	167	27	94.3	94.3	NUM
ajst-25954	167	28	chb04	chb04	PROPN
ajst-25954	167	29	7	7	NUM
ajst-25954	167	30	10	10	NUM
ajst-25954	167	31	-	-	SYM
ajst-25954	167	32	4	4	NUM
ajst-25954	167	33	32	32	NUM
ajst-25954	167	34	97.6	97.6	NUM
ajst-25954	167	35	97.2	97.2	NUM
ajst-25954	167	36	96.2	96.2	NUM
ajst-25954	167	37	chb05	chb05	NOUN
ajst-25954	167	38	5	5	NUM
ajst-25954	167	39	10	10	NUM
ajst-25954	167	40	-	-	SYM
ajst-25954	167	41	4	4	NUM
ajst-25954	167	42	32	32	NUM
ajst-25954	167	43	97.0	97.0	NUM
ajst-25954	167	44	100	100	NUM
ajst-25954	167	45	99.0	99.0	NUM
ajst-25954	167	46	chb06	chb06	PROPN
ajst-25954	167	47	7	7	NUM
ajst-25954	167	48	10	10	NUM
ajst-25954	167	49	-	-	SYM
ajst-25954	167	50	4	4	NUM
ajst-25954	167	51	32	32	NUM
ajst-25954	167	52	98.1	98.1	NUM
ajst-25954	167	53	98.5	98.5	NUM
ajst-25954	167	54	98.5	98.5	NUM
ajst-25954	167	55	chb07	chb07	NOUN
ajst-25954	167	56	3	3	NUM
ajst-25954	167	57	10	10	NUM
ajst-25954	167	58	-	-	SYM
ajst-25954	167	59	4	4	NUM
ajst-25954	167	60	32	32	NUM
ajst-25954	167	61	94.2	94.2	NUM
ajst-25954	167	62	100	100	NUM
ajst-25954	167	63	100	100	NUM
ajst-25954	167	64	chb08	chb08	NOUN
ajst-25954	167	65	5	5	NUM
ajst-25954	167	66	10	10	NUM
ajst-25954	167	67	-	-	SYM
ajst-25954	167	68	4	4	NUM
ajst-25954	167	69	32	32	NUM
ajst-25954	167	70	90.9	90.9	NUM
ajst-25954	167	71	90.2	90.2	NUM
ajst-25954	167	72	88.8	88.8	NUM
ajst-25954	167	73	chb09	chb09	PROPN
ajst-25954	167	74	3	3	NUM
ajst-25954	167	75	10	10	NUM
ajst-25954	167	76	-	-	SYM
ajst-25954	167	77	4	4	NUM
ajst-25954	167	78	32	32	NUM
ajst-25954	167	79	94.5	94.5	NUM
ajst-25954	167	80	96.2	96.2	NUM
ajst-25954	167	81	96.7	96.7	NUM
ajst-25954	167	82	chb10	chb10	NOUN
ajst-25954	167	83	6	6	NUM
ajst-25954	167	84	10	10	NUM
ajst-25954	167	85	-	-	SYM
ajst-25954	167	86	4	4	NUM
ajst-25954	167	87	32	32	NUM
ajst-25954	167	88	100	100	NUM
ajst-25954	167	89	100	100	NUM
ajst-25954	167	90	100	100	NUM
ajst-25954	167	91	chb11	chb11	NOUN
ajst-25954	167	92	3	3	NUM
ajst-25954	167	93	10	10	NUM
ajst-25954	167	94	-	-	SYM
ajst-25954	167	95	4	4	NUM
ajst-25954	167	96	32	32	NUM
ajst-25954	167	97	96.3	96.3	NUM
ajst-25954	167	98	95.8	95.8	NUM
ajst-25954	167	99	97.8	97.8	NUM
ajst-25954	167	100	chb12	chb12	ADJ
ajst-25954	167	101	7	7	NUM
ajst-25954	167	102	10	10	NUM
ajst-25954	167	103	-	-	SYM
ajst-25954	167	104	4	4	NUM
ajst-25954	167	105	32	32	NUM
ajst-25954	167	106	94.8	94.8	NUM
ajst-25954	167	107	91.5	91.5	NUM
ajst-25954	167	108	94.5	94.5	NUM
ajst-25954	167	109	chb13	chb13	ADJ
ajst-25954	167	110	4	4	NUM
ajst-25954	167	111	10	10	NUM
ajst-25954	167	112	-	-	SYM
ajst-25954	167	113	4	4	NUM
ajst-25954	167	114	32	32	NUM
ajst-25954	167	115	99.3	99.3	NUM
ajst-25954	167	116	100	100	NUM
ajst-25954	167	117	100	100	NUM
ajst-25954	167	118	chb14	chb14	NOUN
ajst-25954	167	119	6	6	NUM
ajst-25954	167	120	10	10	NUM
ajst-25954	167	121	-	-	SYM
ajst-25954	167	122	4	4	NUM
ajst-25954	167	123	32	32	NUM
ajst-25954	167	124	100	100	NUM
ajst-25954	167	125	100	100	NUM
ajst-25954	167	126	100	100	NUM
ajst-25954	167	127	chb15	chb15	NUM
ajst-25954	167	128	4	4	NUM
ajst-25954	167	129	10	10	NUM
ajst-25954	167	130	-	-	SYM
ajst-25954	167	131	4	4	NUM
ajst-25954	167	132	32	32	NUM
ajst-25954	167	133	89.9	89.9	NUM
ajst-25954	167	134	90.1	90.1	NUM
ajst-25954	167	135	92	92	NUM
ajst-25954	167	136	chb16	chb16	NOUN
ajst-25954	167	137	5	5	NUM
ajst-25954	167	138	10	10	NUM
ajst-25954	167	139	-	-	SYM
ajst-25954	167	140	4	4	NUM
ajst-25954	167	141	32	32	NUM
ajst-25954	167	142	98.1	98.1	NUM
ajst-25954	167	143	97.7	97.7	NUM
ajst-25954	167	144	97.4	97.4	NUM
ajst-25954	167	145	chb17	chb17	NOUN
ajst-25954	167	146	3	3	NUM
ajst-25954	167	147	10	10	NUM
ajst-25954	167	148	-	-	SYM
ajst-25954	167	149	4	4	NUM
ajst-25954	167	150	32	32	NUM
ajst-25954	167	151	93.9	93.9	NUM
ajst-25954	167	152	89.9	89.9	NUM
ajst-25954	167	153	91.1	91.1	NUM
ajst-25954	167	154	chb18	chb18	NOUN
ajst-25954	167	155	5	5	NUM
ajst-25954	167	156	10	10	NUM
ajst-25954	167	157	-	-	SYM
ajst-25954	167	158	4	4	NUM
ajst-25954	167	159	32	32	NUM
ajst-25954	167	160	100	100	NUM
ajst-25954	167	161	100	100	NUM
ajst-25954	167	162	100	100	NUM
ajst-25954	167	163	chb19	chb19	NOUN
ajst-25954	167	164	3	3	NUM
ajst-25954	167	165	10	10	NUM
ajst-25954	167	166	-	-	SYM
ajst-25954	167	167	4	4	NUM
ajst-25954	167	168	32	32	NUM
ajst-25954	167	169	93.3	93.3	NUM
ajst-25954	167	170	92.7	92.7	NUM
ajst-25954	167	171	94.4	94.4	NUM
ajst-25954	167	172	chb20	chb20	NOUN
ajst-25954	167	173	4	4	NUM
ajst-25954	167	174	10	10	NUM
ajst-25954	167	175	-	-	SYM
ajst-25954	167	176	4	4	NUM
ajst-25954	167	177	32	32	NUM
ajst-25954	167	178	100	100	NUM
ajst-25954	167	179	100	100	NUM
ajst-25954	167	180	100	100	NUM
ajst-25954	167	181	chb21	chb21	NOUN
ajst-25954	167	182	3	3	NUM
ajst-25954	167	183	10	10	NUM
ajst-25954	167	184	-	-	SYM
ajst-25954	167	185	4	4	NUM
ajst-25954	167	186	32	32	NUM
ajst-25954	167	187	100	100	NUM
ajst-25954	167	188	100	100	NUM
ajst-25954	167	189	100	100	NUM
ajst-25954	167	190	chb22	chb22	PROPN
ajst-25954	167	191	6	6	NUM
ajst-25954	167	192	10	10	NUM
ajst-25954	167	193	-	-	SYM
ajst-25954	167	194	4	4	NUM
ajst-25954	167	195	32	32	NUM
ajst-25954	167	196	98.9	98.9	NUM
ajst-25954	167	197	100	100	NUM
ajst-25954	167	198	100	100	NUM
ajst-25954	167	199	chb23	chb23	NOUN
ajst-25954	167	200	5	5	NUM
ajst-25954	167	201	10	10	NUM
ajst-25954	167	202	-	-	SYM
ajst-25954	167	203	4	4	NUM
ajst-25954	167	204	32	32	NUM
ajst-25954	167	205	96.5	96.5	NUM
ajst-25954	167	206	96.8	96.8	NUM
ajst-25954	167	207	96.2	96.2	NUM
ajst-25954	167	208	chb24	chb24	NOUN
ajst-25954	167	209	6	6	NUM
ajst-25954	167	210	10	10	NUM
ajst-25954	167	211	-	-	SYM
ajst-25954	167	212	4	4	NUM
ajst-25954	167	213	32	32	NUM
ajst-25954	167	214	97.4	97.4	NUM
ajst-25954	167	215	95.9	95.9	NUM
ajst-25954	167	216	96.6	96.6	NUM
ajst-25954	167	217	average	average	ADJ
ajst-25954	167	218	---96.8	---96.8	NOUN
ajst-25954	167	219	96.9	96.9	NUM
ajst-25954	167	220	97.2	97.2	NUM
ajst-25954	167	221	4	4	NUM
ajst-25954	167	222	.	.	PUNCT
ajst-25954	168	1	experimental	experimental	ADJ
ajst-25954	168	2	results	result	NOUN
ajst-25954	168	3	and	and	CCONJ
ajst-25954	168	4	analysis	analysis	NOUN
ajst-25954	168	5	1.analysis	1.analysis	NUM
ajst-25954	168	6	and	and	CCONJ
ajst-25954	168	7	comparison	comparison	NOUN
ajst-25954	168	8	of	of	ADP
ajst-25954	168	9	ms	ms	PROPN
ajst-25954	168	10	-	-	PUNCT
ajst-25954	168	11	stfm	stfm	NOUN
ajst-25954	168	12	-	-	PUNCT
ajst-25954	168	13	pcfwnet	pcfwnet	NOUN
ajst-25954	168	14	and	and	CCONJ
ajst-25954	168	15	baseline	baseline	ADJ
ajst-25954	168	16	network	network	NOUN
ajst-25954	168	17	results	result	NOUN
ajst-25954	168	18	in	in	ADP
ajst-25954	168	19	order	order	NOUN
ajst-25954	168	20	to	to	PART
ajst-25954	168	21	verify	verify	VERB
ajst-25954	168	22	the	the	DET
ajst-25954	168	23	learning	learning	NOUN
ajst-25954	168	24	performance	performance	NOUN
ajst-25954	168	25	of	of	ADP
ajst-25954	168	26	the	the	DET
ajst-25954	168	27	msstfm	msstfm	NOUN
ajst-25954	168	28	-	-	PUNCT
ajst-25954	168	29	pcfwnet	pcfwnet	NOUN
ajst-25954	168	30	method	method	NOUN
ajst-25954	168	31	proposed	propose	VERB
ajst-25954	168	32	in	in	ADP
ajst-25954	168	33	this	this	DET
ajst-25954	168	34	paper	paper	NOUN
ajst-25954	168	35	,	,	PUNCT
ajst-25954	168	36	the	the	DET
ajst-25954	168	37	proposed	propose	VERB
ajst-25954	168	38	method	method	NOUN
ajst-25954	168	39	is	be	AUX
ajst-25954	168	40	compared	compare	VERB
ajst-25954	168	41	with	with	ADP
ajst-25954	168	42	the	the	DET
ajst-25954	168	43	performance	performance	NOUN
ajst-25954	168	44	results	result	NOUN
ajst-25954	168	45	of	of	ADP
ajst-25954	168	46	the	the	DET
ajst-25954	168	47	baseline	baseline	NOUN
ajst-25954	168	48	network	network	NOUN
ajst-25954	168	49	on	on	ADP
ajst-25954	168	50	the	the	DET
ajst-25954	168	51	chb	chb	NOUN
ajst-25954	168	52	-	-	PUNCT
ajst-25954	168	53	mit	mit	NOUN
ajst-25954	168	54	and	and	CCONJ
ajst-25954	168	55	bonn	bonn	PROPN
ajst-25954	168	56	datasets	dataset	NOUN
ajst-25954	168	57	.	.	PUNCT
ajst-25954	169	1	table	table	PROPN
ajst-25954	169	2	ii	ii	PROPN
ajst-25954	169	3	shows	show	VERB
ajst-25954	169	4	the	the	DET
ajst-25954	169	5	results	result	NOUN
ajst-25954	169	6	of	of	ADP
ajst-25954	169	7	the	the	DET
ajst-25954	169	8	proposed	propose	VERB
ajst-25954	169	9	seizure	seizure	NOUN
ajst-25954	169	10	prediction	prediction	NOUN
ajst-25954	169	11	method	method	NOUN
ajst-25954	169	12	in	in	ADP
ajst-25954	169	13	the	the	DET
ajst-25954	169	14	chb	chb	NOUN
ajst-25954	169	15	-	-	PUNCT
ajst-25954	169	16	mit	mit	NOUN
ajst-25954	169	17	dataset	dataset	NOUN
ajst-25954	169	18	,	,	PUNCT
ajst-25954	169	19	which	which	PRON
ajst-25954	169	20	achieve	achieve	VERB
ajst-25954	169	21	a	a	DET
ajst-25954	169	22	87	87	NUM
ajst-25954	169	23	mean	mean	NOUN
ajst-25954	169	24	accuracy	accuracy	NOUN
ajst-25954	169	25	of	of	ADP
ajst-25954	169	26	96.8	96.8	NUM
ajst-25954	169	27	%	%	NOUN
ajst-25954	169	28	,	,	PUNCT
ajst-25954	169	29	a	a	DET
ajst-25954	169	30	mean	mean	ADJ
ajst-25954	169	31	specificity	specificity	NOUN
ajst-25954	169	32	of	of	ADP
ajst-25954	169	33	96.9	96.9	NUM
ajst-25954	169	34	%	%	NOUN
ajst-25954	169	35	,	,	PUNCT
ajst-25954	169	36	and	and	CCONJ
ajst-25954	169	37	a	a	DET
ajst-25954	169	38	mean	mean	ADJ
ajst-25954	169	39	sensitivity	sensitivity	NOUN
ajst-25954	169	40	of	of	ADP
ajst-25954	169	41	97.2	97.2	NUM
ajst-25954	169	42	%	%	NOUN
ajst-25954	169	43	.	.	PUNCT
ajst-25954	170	1	for	for	ADP
ajst-25954	170	2	all	all	DET
ajst-25954	170	3	the	the	DET
ajst-25954	170	4	epileptic	epileptic	ADJ
ajst-25954	170	5	and	and	CCONJ
ajst-25954	170	6	nonepileptic	nonepileptic	ADJ
ajst-25954	170	7	classification	classification	NOUN
ajst-25954	170	8	results	result	NOUN
ajst-25954	170	9	considered	consider	VERB
ajst-25954	170	10	on	on	ADP
ajst-25954	170	11	the	the	DET
ajst-25954	170	12	bonn	bonn	PROPN
ajst-25954	170	13	dataset	dataset	NOUN
ajst-25954	170	14	,	,	PUNCT
ajst-25954	170	15	as	as	SCONJ
ajst-25954	170	16	shown	show	VERB
ajst-25954	170	17	in	in	ADP
ajst-25954	170	18	table	table	NOUN
ajst-25954	170	19	iii	iii	NOUN
ajst-25954	170	20	.	.	PUNCT
ajst-25954	171	1	the	the	DET
ajst-25954	171	2	accuracy	accuracy	NOUN
ajst-25954	171	3	,	,	PUNCT
ajst-25954	171	4	specificity	specificity	NOUN
ajst-25954	171	5	and	and	CCONJ
ajst-25954	171	6	sensitivity	sensitivity	NOUN
ajst-25954	171	7	of	of	ADP
ajst-25954	171	8	the	the	DET
ajst-25954	171	9	method	method	NOUN
ajst-25954	171	10	on	on	ADP
ajst-25954	171	11	the	the	DET
ajst-25954	171	12	bonn	bonn	PROPN
ajst-25954	171	13	dataset	dataset	PROPN
ajst-25954	171	14	achieves	achieve	VERB
ajst-25954	171	15	100	100	NUM
ajst-25954	171	16	%	%	NOUN
ajst-25954	171	17	.	.	PUNCT
ajst-25954	172	1	the	the	DET
ajst-25954	172	2	ms	ms	PROPN
ajst-25954	172	3	-	-	PUNCT
ajst-25954	172	4	stfm	stfm	NOUN
ajst-25954	172	5	-	-	PUNCT
ajst-25954	172	6	pcfwnet	pcfwnet	NOUN
ajst-25954	172	7	model	model	NOUN
ajst-25954	172	8	reaches	reach	VERB
ajst-25954	172	9	good	good	ADJ
ajst-25954	172	10	results	result	NOUN
ajst-25954	172	11	with	with	ADP
ajst-25954	172	12	a	a	DET
ajst-25954	172	13	learning	learn	VERB
ajst-25954	172	14	rate	rate	NOUN
ajst-25954	172	15	of	of	ADP
ajst-25954	172	16	0.0001	0.0001	NUM
ajst-25954	172	17	.	.	PUNCT
ajst-25954	173	1	table	table	NOUN
ajst-25954	173	2	iii	iii	PROPN
ajst-25954	173	3	.	.	PUNCT
ajst-25954	174	1	the	the	DET
ajst-25954	174	2	proposed	propose	VERB
ajst-25954	174	3	method	method	NOUN
ajst-25954	174	4	results	result	NOUN
ajst-25954	174	5	in	in	ADP
ajst-25954	174	6	data	datum	NOUN
ajst-25954	174	7	on	on	ADP
ajst-25954	174	8	the	the	DET
ajst-25954	174	9	bonn	bonn	PROPN
ajst-25954	174	10	dataset	dataset	PROPN
ajst-25954	174	11	cases	case	NOUN
ajst-25954	174	12	lr	lr	NOUN
ajst-25954	174	13	batch	batch	NOUN
ajst-25954	174	14	size	size	NOUN
ajst-25954	174	15	acc(%	acc(%	NOUN
ajst-25954	174	16	)	)	PUNCT
ajst-25954	174	17	spe(%	spe(%	NOUN
ajst-25954	174	18	)	)	PUNCT
ajst-25954	174	19	sen(%	sen(%	PROPN
ajst-25954	174	20	)	)	PUNCT
ajst-25954	174	21	a_e	a_e	PUNCT
ajst-25954	175	1	10	10	NUM
ajst-25954	175	2	-	-	SYM
ajst-25954	175	3	4	4	NUM
ajst-25954	175	4	32	32	NUM
ajst-25954	175	5	100	100	NUM
ajst-25954	175	6	100	100	NUM
ajst-25954	175	7	100	100	NUM
ajst-25954	175	8	b_e	b_e	ADP
ajst-25954	175	9	10	10	NUM
ajst-25954	175	10	-	-	SYM
ajst-25954	175	11	4	4	NUM
ajst-25954	175	12	32	32	NUM
ajst-25954	175	13	100	100	NUM
ajst-25954	175	14	100	100	NUM
ajst-25954	175	15	100	100	NUM
ajst-25954	175	16	c_e	c_e	NUM
ajst-25954	175	17	10	10	NUM
ajst-25954	175	18	-	-	SYM
ajst-25954	175	19	4	4	NUM
ajst-25954	175	20	32	32	NUM
ajst-25954	175	21	100	100	NUM
ajst-25954	175	22	100	100	NUM
ajst-25954	175	23	100	100	NUM
ajst-25954	175	24	d_e	d_e	PROPN
ajst-25954	175	25	10	10	NUM
ajst-25954	175	26	-	-	SYM
ajst-25954	175	27	4	4	NUM
ajst-25954	175	28	32	32	NUM
ajst-25954	175	29	100	100	NUM
ajst-25954	175	30	100	100	NUM
ajst-25954	175	31	100	100	NUM
ajst-25954	175	32	abcd_e	abcd_e	NOUN
ajst-25954	175	33	10	10	NUM
ajst-25954	175	34	-	-	SYM
ajst-25954	175	35	4	4	NUM
ajst-25954	175	36	32	32	NUM
ajst-25954	175	37	100	100	NUM
ajst-25954	175	38	100	100	NUM
ajst-25954	175	39	100	100	NUM
ajst-25954	175	40	in	in	ADP
ajst-25954	175	41	this	this	DET
ajst-25954	175	42	experiment	experiment	NOUN
ajst-25954	175	43	,	,	PUNCT
ajst-25954	175	44	the	the	DET
ajst-25954	175	45	ms	ms	PROPN
ajst-25954	175	46	-	-	PUNCT
ajst-25954	175	47	stfm	stfm	NOUN
ajst-25954	175	48	-	-	PUNCT
ajst-25954	175	49	pcfwnet	pcfwnet	NOUN
ajst-25954	175	50	model	model	NOUN
ajst-25954	175	51	is	be	AUX
ajst-25954	175	52	compared	compare	VERB
ajst-25954	175	53	with	with	ADP
ajst-25954	175	54	various	various	ADJ
ajst-25954	175	55	baseline	baseline	NOUN
ajst-25954	175	56	network	network	NOUN
ajst-25954	175	57	models	model	NOUN
ajst-25954	175	58	(	(	PUNCT
ajst-25954	175	59	1d	1d	NUM
ajst-25954	175	60	-	-	PUNCT
ajst-25954	175	61	cnn	cnn	PROPN
ajst-25954	175	62	,	,	PUNCT
ajst-25954	175	63	mlp	mlp	PROPN
ajst-25954	175	64	,	,	PUNCT
ajst-25954	175	65	svm	svm	PROPN
ajst-25954	175	66	,	,	PUNCT
ajst-25954	175	67	pcnn	pcnn	PROPN
ajst-25954	175	68	)	)	PUNCT
ajst-25954	175	69	.	.	PUNCT
ajst-25954	176	1	as	as	SCONJ
ajst-25954	176	2	shown	show	VERB
ajst-25954	176	3	in	in	ADP
ajst-25954	176	4	fig	fig	NOUN
ajst-25954	176	5	.	.	PUNCT
ajst-25954	177	1	5	5	NUM
ajst-25954	177	2	and	and	CCONJ
ajst-25954	177	3	fig	fig	NOUN
ajst-25954	177	4	.	.	PUNCT
ajst-25954	178	1	6	6	NUM
ajst-25954	178	2	,	,	PUNCT
ajst-25954	178	3	for	for	ADP
ajst-25954	178	4	the	the	DET
ajst-25954	178	5	chb	chb	NOUN
ajst-25954	178	6	-	-	PUNCT
ajst-25954	178	7	mit	mit	NOUN
ajst-25954	178	8	and	and	CCONJ
ajst-25954	178	9	bonn	bonn	PROPN
ajst-25954	178	10	datasets	dataset	NOUN
ajst-25954	178	11	,	,	PUNCT
ajst-25954	178	12	the	the	DET
ajst-25954	178	13	ms	ms	PROPN
ajst-25954	178	14	-	-	PUNCT
ajst-25954	178	15	stfm	stfm	NOUN
ajst-25954	178	16	-	-	PUNCT
ajst-25954	178	17	pcfwnet	pcfwnet	NOUN
ajst-25954	178	18	is	be	AUX
ajst-25954	178	19	superior	superior	ADJ
ajst-25954	178	20	to	to	ADP
ajst-25954	178	21	the	the	DET
ajst-25954	178	22	baseline	baseline	ADJ
ajst-25954	178	23	network	network	NOUN
ajst-25954	178	24	in	in	ADP
ajst-25954	178	25	the	the	DET
ajst-25954	178	26	prediction	prediction	NOUN
ajst-25954	178	27	task	task	NOUN
ajst-25954	178	28	.	.	PUNCT
ajst-25954	179	1	in	in	ADP
ajst-25954	179	2	particular	particular	ADJ
ajst-25954	179	3	,	,	PUNCT
ajst-25954	179	4	for	for	ADP
ajst-25954	179	5	both	both	PRON
ajst-25954	179	6	chb	chb	NOUN
ajst-25954	179	7	-	-	PUNCT
ajst-25954	179	8	mit	mit	NOUN
ajst-25954	179	9	and	and	CCONJ
ajst-25954	179	10	bonn	bonn	PROPN
ajst-25954	179	11	datasets	dataset	NOUN
ajst-25954	179	12	,	,	PUNCT
ajst-25954	179	13	the	the	DET
ajst-25954	179	14	accuracy	accuracy	NOUN
ajst-25954	179	15	of	of	ADP
ajst-25954	179	16	the	the	DET
ajst-25954	179	17	proposed	propose	VERB
ajst-25954	179	18	methods	method	NOUN
ajst-25954	179	19	is	be	AUX
ajst-25954	179	20	better	well	ADJ
ajst-25954	179	21	than	than	ADP
ajst-25954	179	22	the	the	DET
ajst-25954	179	23	baseline	baseline	NOUN
ajst-25954	179	24	network	network	NOUN
ajst-25954	179	25	.	.	PUNCT
ajst-25954	180	1	in	in	ADP
ajst-25954	180	2	contrast	contrast	NOUN
ajst-25954	180	3	,	,	PUNCT
ajst-25954	180	4	the	the	DET
ajst-25954	180	5	proposed	propose	VERB
ajst-25954	180	6	method	method	NOUN
ajst-25954	180	7	has	have	VERB
ajst-25954	180	8	good	good	ADJ
ajst-25954	180	9	learning	learning	NOUN
ajst-25954	180	10	performance	performance	NOUN
ajst-25954	180	11	.	.	PUNCT
ajst-25954	181	1	figure	figure	NOUN
ajst-25954	181	2	5	5	NUM
ajst-25954	181	3	.	.	PUNCT
ajst-25954	182	1	the	the	DET
ajst-25954	182	2	average	average	ADJ
ajst-25954	182	3	accuracy	accuracy	NOUN
ajst-25954	182	4	of	of	ADP
ajst-25954	182	5	the	the	DET
ajst-25954	182	6	method	method	NOUN
ajst-25954	182	7	is	be	AUX
ajst-25954	182	8	compared	compare	VERB
ajst-25954	182	9	with	with	ADP
ajst-25954	182	10	the	the	DET
ajst-25954	182	11	baseline	baseline	NOUN
ajst-25954	182	12	network	network	NOUN
ajst-25954	182	13	.	.	PUNCT
ajst-25954	183	1	chb‐mit	chb‐mit	X
ajst-25954	183	2	bonn	bonn	PROPN
ajst-25954	183	3	figure	figure	NOUN
ajst-25954	183	4	6	6	NUM
ajst-25954	183	5	.	.	PUNCT
ajst-25954	184	1	performance	performance	NOUN
ajst-25954	184	2	comparison	comparison	NOUN
ajst-25954	184	3	between	between	ADP
ajst-25954	184	4	the	the	DET
ajst-25954	184	5	proposed	propose	VERB
ajst-25954	184	6	method	method	NOUN
ajst-25954	184	7	and	and	CCONJ
ajst-25954	184	8	the	the	DET
ajst-25954	184	9	baseline	baseline	NOUN
ajst-25954	184	10	network	network	NOUN
ajst-25954	184	11	on	on	ADP
ajst-25954	184	12	chb	chb	NOUN
ajst-25954	184	13	-	-	PUNCT
ajst-25954	184	14	mit	mit	NOUN
ajst-25954	184	15	and	and	CCONJ
ajst-25954	184	16	bonn	bonn	NOUN
ajst-25954	184	17	.	.	PROPN
ajst-25954	185	1	1	1	X
ajst-25954	185	2	.	.	X
ajst-25954	185	3	comparative	comparative	ADJ
ajst-25954	185	4	analysis	analysis	NOUN
ajst-25954	185	5	of	of	ADP
ajst-25954	185	6	the	the	DET
ajst-25954	185	7	results	result	NOUN
ajst-25954	185	8	of	of	ADP
ajst-25954	185	9	ms	ms	NOUN
ajst-25954	185	10	-	-	PUNCT
ajst-25954	185	11	stfmpcfwnet	stfmpcfwnet	NOUN
ajst-25954	185	12	and	and	CCONJ
ajst-25954	185	13	the	the	DET
ajst-25954	185	14	original	original	ADJ
ajst-25954	185	15	model	model	NOUN
ajst-25954	185	16	(	(	PUNCT
ajst-25954	185	17	1	1	NUM
ajst-25954	185	18	)	)	PUNCT
ajst-25954	185	19	influence	influence	NOUN
ajst-25954	185	20	of	of	ADP
ajst-25954	185	21	multi	multi	ADJ
ajst-25954	185	22	-	-	ADJ
ajst-25954	185	23	scale	scale	ADJ
ajst-25954	185	24	feature	feature	NOUN
ajst-25954	185	25	convolution	convolution	NOUN
ajst-25954	185	26	in	in	ADP
ajst-25954	185	27	order	order	NOUN
ajst-25954	185	28	to	to	PART
ajst-25954	185	29	further	further	VERB
ajst-25954	185	30	intuitively	intuitively	ADV
ajst-25954	185	31	verify	verify	VERB
ajst-25954	185	32	the	the	DET
ajst-25954	185	33	superiority	superiority	NOUN
ajst-25954	185	34	of	of	ADP
ajst-25954	185	35	the	the	DET
ajst-25954	185	36	proposed	propose	VERB
ajst-25954	185	37	fm	fm	PROPN
ajst-25954	185	38	feature	feature	NOUN
ajst-25954	185	39	extraction	extraction	NOUN
ajst-25954	185	40	,	,	PUNCT
ajst-25954	185	41	the	the	DET
ajst-25954	185	42	scatterplot	scatterplot	NOUN
ajst-25954	185	43	is	be	AUX
ajst-25954	185	44	utilized	utilize	VERB
ajst-25954	185	45	to	to	PART
ajst-25954	185	46	compare	compare	VERB
ajst-25954	185	47	ms	ms	PROPN
ajst-25954	185	48	-	-	PUNCT
ajst-25954	185	49	stfm	stfm	NOUN
ajst-25954	185	50	-	-	PUNCT
ajst-25954	185	51	pcfwnet	pcfwnet	NOUN
ajst-25954	185	52	with	with	ADP
ajst-25954	185	53	a	a	DET
ajst-25954	185	54	simplified	simplified	ADJ
ajst-25954	185	55	model	model	NOUN
ajst-25954	185	56	without	without	ADP
ajst-25954	185	57	fm	fm	PROPN
ajst-25954	185	58	feature	feature	NOUN
ajst-25954	185	59	extraction	extraction	NOUN
ajst-25954	185	60	.	.	PUNCT
ajst-25954	186	1	the	the	DET
ajst-25954	186	2	visual	visual	ADJ
ajst-25954	186	3	scatter	scatter	NOUN
ajst-25954	186	4	plots	plot	NOUN
ajst-25954	186	5	of	of	ADP
ajst-25954	186	6	interictal	interictal	ADJ
ajst-25954	186	7	and	and	CCONJ
ajst-25954	186	8	preictal	preictal	ADJ
ajst-25954	186	9	features	feature	NOUN
ajst-25954	186	10	on	on	ADP
ajst-25954	186	11	the	the	DET
ajst-25954	186	12	two	two	NUM
ajst-25954	186	13	datasets	dataset	NOUN
ajst-25954	186	14	are	be	AUX
ajst-25954	186	15	shown	show	VERB
ajst-25954	186	16	88	88	NUM
ajst-25954	186	17	in	in	ADP
ajst-25954	186	18	fig	fig	NOUN
ajst-25954	186	19	.	.	PUNCT
ajst-25954	187	1	7	7	X
ajst-25954	187	2	.	.	X
ajst-25954	187	3	where	where	SCONJ
ajst-25954	187	4	fig	fig	NOUN
ajst-25954	187	5	.	.	PUNCT
ajst-25954	188	1	7(a	7(a	NUM
ajst-25954	188	2	)	)	PUNCT
ajst-25954	189	1	and	and	CCONJ
ajst-25954	189	2	(	(	PUNCT
ajst-25954	189	3	b	b	NOUN
ajst-25954	189	4	)	)	PUNCT
ajst-25954	189	5	respectively	respectively	ADV
ajst-25954	189	6	show	show	VERB
ajst-25954	189	7	the	the	DET
ajst-25954	189	8	output	output	NOUN
ajst-25954	189	9	of	of	ADP
ajst-25954	189	10	fm	fm	PROPN
ajst-25954	189	11	and	and	CCONJ
ajst-25954	189	12	without	without	ADP
ajst-25954	189	13	fm	fm	PROPN
ajst-25954	189	14	on	on	ADP
ajst-25954	189	15	the	the	DET
ajst-25954	189	16	chb	chb	NOUN
ajst-25954	189	17	-	-	PUNCT
ajst-25954	189	18	mit	mit	NOUN
ajst-25954	189	19	dataset	dataset	NOUN
ajst-25954	189	20	,	,	PUNCT
ajst-25954	189	21	and	and	CCONJ
ajst-25954	189	22	figure	figure	NOUN
ajst-25954	189	23	(	(	PUNCT
ajst-25954	189	24	c	c	NOUN
ajst-25954	189	25	)	)	PUNCT
ajst-25954	189	26	and	and	CCONJ
ajst-25954	189	27	(	(	PUNCT
ajst-25954	189	28	e	e	NOUN
ajst-25954	189	29	)	)	PUNCT
ajst-25954	189	30	respectively	respectively	ADV
ajst-25954	189	31	show	show	VERB
ajst-25954	189	32	the	the	DET
ajst-25954	189	33	output	output	NOUN
ajst-25954	189	34	of	of	ADP
ajst-25954	189	35	fm	fm	PROPN
ajst-25954	189	36	and	and	CCONJ
ajst-25954	189	37	without	without	ADP
ajst-25954	189	38	fm	fm	PROPN
ajst-25954	189	39	on	on	ADP
ajst-25954	189	40	the	the	DET
ajst-25954	189	41	bonn	bonn	PROPN
ajst-25954	189	42	dataset	dataset	NOUN
ajst-25954	189	43	.	.	PUNCT
ajst-25954	190	1	it	it	PRON
ajst-25954	190	2	can	can	AUX
ajst-25954	190	3	be	be	AUX
ajst-25954	190	4	seen	see	VERB
ajst-25954	190	5	that	that	SCONJ
ajst-25954	190	6	the	the	DET
ajst-25954	190	7	feature	feature	NOUN
ajst-25954	190	8	distribution	distribution	NOUN
ajst-25954	190	9	of	of	ADP
ajst-25954	190	10	binary	binary	ADJ
ajst-25954	190	11	classification	classification	NOUN
ajst-25954	190	12	using	use	VERB
ajst-25954	190	13	fm	fm	PROPN
ajst-25954	190	14	feature	feature	NOUN
ajst-25954	190	15	extraction	extraction	NOUN
ajst-25954	190	16	is	be	AUX
ajst-25954	190	17	more	more	ADV
ajst-25954	190	18	distinguishable	distinguishable	ADJ
ajst-25954	190	19	than	than	ADP
ajst-25954	190	20	that	that	PRON
ajst-25954	190	21	without	without	ADP
ajst-25954	190	22	fm	fm	PROPN
ajst-25954	190	23	.	.	PROPN
ajst-25954	191	1	in	in	ADP
ajst-25954	191	2	particular	particular	ADJ
ajst-25954	191	3	,	,	PUNCT
ajst-25954	191	4	for	for	ADP
ajst-25954	191	5	the	the	DET
ajst-25954	191	6	chb	chb	NOUN
ajst-25954	191	7	-	-	PUNCT
ajst-25954	191	8	mit	mit	NOUN
ajst-25954	191	9	and	and	CCONJ
ajst-25954	191	10	bonn	bonn	PROPN
ajst-25954	191	11	datasets	dataset	NOUN
ajst-25954	191	12	,	,	PUNCT
ajst-25954	191	13	without	without	ADP
ajst-25954	191	14	a	a	DET
ajst-25954	191	15	model	model	NOUN
ajst-25954	191	16	using	use	VERB
ajst-25954	191	17	fm	fm	PROPN
ajst-25954	191	18	feature	feature	NOUN
ajst-25954	191	19	extraction	extraction	NOUN
ajst-25954	191	20	,	,	PUNCT
ajst-25954	191	21	a	a	DET
ajst-25954	191	22	portion	portion	NOUN
ajst-25954	191	23	of	of	ADP
ajst-25954	191	24	the	the	DET
ajst-25954	191	25	interseizure	interseizure	NOUN
ajst-25954	191	26	and	and	CCONJ
ajst-25954	191	27	pre	pre	VERB
ajst-25954	191	28	-	-	ADJ
ajst-25954	191	29	seizure	seizure	ADJ
ajst-25954	191	30	features	feature	NOUN
ajst-25954	191	31	would	would	AUX
ajst-25954	191	32	be	be	AUX
ajst-25954	191	33	confused	confuse	VERB
ajst-25954	191	34	.	.	PUNCT
ajst-25954	192	1	in	in	ADP
ajst-25954	192	2	contrast	contrast	NOUN
ajst-25954	192	3	,	,	PUNCT
ajst-25954	192	4	models	model	NOUN
ajst-25954	192	5	utilize	utilize	VERB
ajst-25954	192	6	fm	fm	PROPN
ajst-25954	192	7	feature	feature	NOUN
ajst-25954	192	8	extraction	extraction	NOUN
ajst-25954	192	9	obtained	obtain	VERB
ajst-25954	192	10	more	more	ADJ
ajst-25954	192	11	discriminating	discriminating	ADJ
ajst-25954	192	12	features	feature	NOUN
ajst-25954	192	13	.	.	PUNCT
ajst-25954	193	1	this	this	PRON
ajst-25954	193	2	shows	show	VERB
ajst-25954	193	3	that	that	SCONJ
ajst-25954	193	4	the	the	DET
ajst-25954	193	5	combination	combination	NOUN
ajst-25954	193	6	of	of	ADP
ajst-25954	193	7	multi	multi	ADJ
ajst-25954	193	8	-	-	ADJ
ajst-25954	193	9	scale	scale	ADJ
ajst-25954	193	10	convolution	convolution	NOUN
ajst-25954	193	11	and	and	CCONJ
ajst-25954	193	12	3d	3d	NUM
ajst-25954	193	13	feature	feature	NOUN
ajst-25954	193	14	matrix	matrix	NOUN
ajst-25954	193	15	can	can	AUX
ajst-25954	193	16	produce	produce	VERB
ajst-25954	193	17	good	good	ADJ
ajst-25954	193	18	seizure	seizure	NOUN
ajst-25954	193	19	prediction	prediction	NOUN
ajst-25954	193	20	performance	performance	NOUN
ajst-25954	193	21	,	,	PUNCT
ajst-25954	193	22	which	which	PRON
ajst-25954	193	23	also	also	ADV
ajst-25954	193	24	fully	fully	ADV
ajst-25954	193	25	illustrates	illustrate	VERB
ajst-25954	193	26	the	the	DET
ajst-25954	193	27	innovation	innovation	NOUN
ajst-25954	193	28	of	of	ADP
ajst-25954	193	29	fm	fm	PROPN
ajst-25954	193	30	feature	feature	NOUN
ajst-25954	193	31	extraction	extraction	NOUN
ajst-25954	193	32	in	in	ADP
ajst-25954	193	33	spatiotemporal	spatiotemporal	ADJ
ajst-25954	193	34	feature	feature	NOUN
ajst-25954	193	35	extraction	extraction	NOUN
ajst-25954	193	36	.	.	PUNCT
ajst-25954	194	1	without	without	ADP
ajst-25954	194	2	fm	fm	PROPN
ajst-25954	194	3	fm	fm	PROPN
ajst-25954	194	4	interictal	interictal	PROPN
ajst-25954	194	5	preictal	preictal	ADJ
ajst-25954	194	6	interictal	interictal	ADJ
ajst-25954	194	7	preictal	preictal	ADJ
ajst-25954	194	8	c	c	PROPN
ajst-25954	194	9	h	h	PROPN
ajst-25954	194	10	b	b	PROPN
ajst-25954	194	11	-m	-m	PUNCT
ajst-25954	194	12	it	it	PRON
ajst-25954	194	13	b	b	NOUN
ajst-25954	194	14	o	o	NOUN
ajst-25954	194	15	n	n	CCONJ
ajst-25954	194	16	n	n	PROPN
ajst-25954	194	17	(	(	PUNCT
ajst-25954	194	18	a	a	NOUN
ajst-25954	194	19	)	)	PUNCT
ajst-25954	194	20	(	(	PUNCT
ajst-25954	194	21	b	b	X
ajst-25954	194	22	)	)	PUNCT
ajst-25954	194	23	(	(	PUNCT
ajst-25954	194	24	c	c	X
ajst-25954	194	25	)	)	PUNCT
ajst-25954	194	26	(	(	PUNCT
ajst-25954	194	27	d	d	X
ajst-25954	194	28	)	)	PUNCT
ajst-25954	194	29	figure	figure	NOUN
ajst-25954	194	30	7	7	NUM
ajst-25954	194	31	.	.	PUNCT
ajst-25954	194	32	visual	visual	ADJ
ajst-25954	194	33	interictal	interictal	NOUN
ajst-25954	194	34	and	and	CCONJ
ajst-25954	194	35	preictal	preictal	ADJ
ajst-25954	194	36	features	feature	NOUN
ajst-25954	194	37	of	of	ADP
ajst-25954	194	38	fm	fm	PROPN
ajst-25954	194	39	and	and	CCONJ
ajst-25954	194	40	fm	fm	PROPN
ajst-25954	194	41	are	be	AUX
ajst-25954	194	42	not	not	PART
ajst-25954	194	43	compared	compare	VERB
ajst-25954	194	44	using	use	VERB
ajst-25954	194	45	scatter	scatter	NOUN
ajst-25954	194	46	plots	plot	NOUN
ajst-25954	194	47	.	.	PUNCT
ajst-25954	195	1	(	(	PUNCT
ajst-25954	195	2	2	2	X
ajst-25954	195	3	)	)	PUNCT
ajst-25954	195	4	the	the	DET
ajst-25954	195	5	influence	influence	NOUN
ajst-25954	195	6	of	of	ADP
ajst-25954	195	7	simplified	simplified	ADJ
ajst-25954	195	8	model	model	NOUN
ajst-25954	195	9	on	on	ADP
ajst-25954	195	10	model	model	NOUN
ajst-25954	195	11	learning	learn	VERB
ajst-25954	195	12	performance	performance	NOUN
ajst-25954	195	13	in	in	ADP
ajst-25954	195	14	order	order	NOUN
ajst-25954	195	15	to	to	PART
ajst-25954	195	16	verify	verify	VERB
ajst-25954	195	17	the	the	DET
ajst-25954	195	18	learning	learning	NOUN
ajst-25954	195	19	ability	ability	NOUN
ajst-25954	195	20	and	and	CCONJ
ajst-25954	195	21	performance	performance	NOUN
ajst-25954	195	22	of	of	ADP
ajst-25954	195	23	the	the	DET
ajst-25954	195	24	model	model	NOUN
ajst-25954	195	25	,	,	PUNCT
ajst-25954	195	26	the	the	DET
ajst-25954	195	27	network	network	NOUN
ajst-25954	195	28	structure	structure	NOUN
ajst-25954	195	29	of	of	ADP
ajst-25954	195	30	the	the	DET
ajst-25954	195	31	proposed	propose	VERB
ajst-25954	195	32	model	model	NOUN
ajst-25954	195	33	and	and	CCONJ
ajst-25954	195	34	three	three	NUM
ajst-25954	195	35	groups	group	NOUN
ajst-25954	195	36	of	of	ADP
ajst-25954	195	37	simplified	simplify	VERB
ajst-25954	195	38	models	model	NOUN
ajst-25954	195	39	are	be	AUX
ajst-25954	195	40	recorded	record	VERB
ajst-25954	195	41	.	.	PUNCT
ajst-25954	196	1	among	among	ADP
ajst-25954	196	2	them	they	PRON
ajst-25954	196	3	,	,	PUNCT
ajst-25954	196	4	the	the	DET
ajst-25954	196	5	network	network	NOUN
ajst-25954	196	6	structure	structure	NOUN
ajst-25954	196	7	of	of	ADP
ajst-25954	196	8	the	the	DET
ajst-25954	196	9	four	four	NUM
ajst-25954	196	10	groups	group	NOUN
ajst-25954	196	11	of	of	ADP
ajst-25954	196	12	models	model	NOUN
ajst-25954	196	13	is	be	AUX
ajst-25954	196	14	consistent	consistent	ADJ
ajst-25954	196	15	,	,	PUNCT
ajst-25954	196	16	but	but	CCONJ
ajst-25954	196	17	pcnn	pcnn	NOUN
ajst-25954	196	18	-	-	PUNCT
ajst-25954	196	19	fmnet	fmnet	PROPN
ajst-25954	196	20	and	and	CCONJ
ajst-25954	196	21	pcnn	pcnn	PROPN
ajst-25954	196	22	-	-	PUNCT
ajst-25954	196	23	ecanet	ecanet	NOUN
ajst-25954	196	24	respectively	respectively	ADV
ajst-25954	196	25	add	add	VERB
ajst-25954	196	26	fm	fm	PROPN
ajst-25954	196	27	and	and	CCONJ
ajst-25954	196	28	eca	eca	NOUN
ajst-25954	196	29	module	module	NOUN
ajst-25954	196	30	to	to	ADP
ajst-25954	196	31	the	the	DET
ajst-25954	196	32	pcnn	pcnn	PROPN
ajst-25954	196	33	model	model	NOUN
ajst-25954	196	34	,	,	PUNCT
ajst-25954	196	35	as	as	SCONJ
ajst-25954	196	36	shown	show	VERB
ajst-25954	196	37	in	in	ADP
ajst-25954	196	38	table	table	NOUN
ajst-25954	196	39	iv	iv	NOUN
ajst-25954	196	40	.	.	PUNCT
ajst-25954	197	1	in	in	ADP
ajst-25954	197	2	addition	addition	NOUN
ajst-25954	197	3	,	,	PUNCT
ajst-25954	197	4	the	the	DET
ajst-25954	197	5	proposed	propose	VERB
ajst-25954	197	6	model	model	NOUN
ajst-25954	197	7	and	and	CCONJ
ajst-25954	197	8	three	three	NUM
ajst-25954	197	9	groups	group	NOUN
ajst-25954	197	10	of	of	ADP
ajst-25954	197	11	simplified	simplify	VERB
ajst-25954	197	12	models	model	NOUN
ajst-25954	197	13	are	be	AUX
ajst-25954	197	14	recorded	record	VERB
ajst-25954	197	15	,	,	PUNCT
ajst-25954	197	16	which	which	PRON
ajst-25954	197	17	the	the	DET
ajst-25954	197	18	accuracy	accuracy	NOUN
ajst-25954	197	19	of	of	ADP
ajst-25954	197	20	the	the	DET
ajst-25954	197	21	first	first	ADJ
ajst-25954	197	22	300	300	NUM
ajst-25954	197	23	training	training	NOUN
ajst-25954	197	24	cycles	cycle	NOUN
ajst-25954	197	25	of	of	ADP
ajst-25954	197	26	the	the	DET
ajst-25954	197	27	training	training	NOUN
ajst-25954	197	28	set	set	NOUN
ajst-25954	197	29	and	and	CCONJ
ajst-25954	197	30	the	the	DET
ajst-25954	197	31	verification	verification	NOUN
ajst-25954	197	32	set	set	NOUN
ajst-25954	197	33	are	be	AUX
ajst-25954	197	34	respectively	respectively	ADV
ajst-25954	197	35	employed	employ	VERB
ajst-25954	197	36	on	on	ADP
ajst-25954	197	37	the	the	DET
ajst-25954	197	38	chbmit	chbmit	NOUN
ajst-25954	197	39	dataset	dataset	VERB
ajst-25954	197	40	,	,	PUNCT
ajst-25954	197	41	as	as	SCONJ
ajst-25954	197	42	shown	show	VERB
ajst-25954	197	43	in	in	ADP
ajst-25954	197	44	fig	fig	NOUN
ajst-25954	197	45	.	.	PUNCT
ajst-25954	198	1	8	8	NUM
ajst-25954	198	2	.	.	X
ajst-25954	199	1	when	when	SCONJ
ajst-25954	199	2	the	the	DET
ajst-25954	199	3	training	training	NOUN
ajst-25954	199	4	cycles	cycle	NOUN
ajst-25954	199	5	are	be	AUX
ajst-25954	199	6	less	less	ADJ
ajst-25954	199	7	than	than	ADP
ajst-25954	199	8	50	50	NUM
ajst-25954	199	9	,	,	PUNCT
ajst-25954	199	10	the	the	DET
ajst-25954	199	11	prediction	prediction	NOUN
ajst-25954	199	12	accuracy	accuracy	NOUN
ajst-25954	199	13	of	of	ADP
ajst-25954	199	14	the	the	DET
ajst-25954	199	15	four	four	NUM
ajst-25954	199	16	groups	group	NOUN
ajst-25954	199	17	of	of	ADP
ajst-25954	199	18	models	model	NOUN
ajst-25954	199	19	is	be	AUX
ajst-25954	199	20	continuously	continuously	ADV
ajst-25954	199	21	improved	improve	VERB
ajst-25954	199	22	,	,	PUNCT
ajst-25954	199	23	when	when	SCONJ
ajst-25954	199	24	the	the	DET
ajst-25954	199	25	training	training	NOUN
ajst-25954	199	26	period	period	NOUN
ajst-25954	199	27	is	be	AUX
ajst-25954	199	28	greater	great	ADJ
ajst-25954	199	29	than	than	ADP
ajst-25954	199	30	50	50	NUM
ajst-25954	199	31	,	,	PUNCT
ajst-25954	199	32	the	the	DET
ajst-25954	199	33	accuracy	accuracy	NOUN
ajst-25954	199	34	is	be	AUX
ajst-25954	199	35	no	no	ADV
ajst-25954	199	36	longer	long	ADV
ajst-25954	199	37	increased	increase	VERB
ajst-25954	199	38	.	.	PUNCT
ajst-25954	200	1	compared	compare	VERB
ajst-25954	200	2	with	with	ADP
ajst-25954	200	3	the	the	DET
ajst-25954	200	4	proposed	propose	VERB
ajst-25954	200	5	model	model	NOUN
ajst-25954	200	6	,	,	PUNCT
ajst-25954	200	7	the	the	DET
ajst-25954	200	8	three	three	NUM
ajst-25954	200	9	simplified	simplified	ADJ
ajst-25954	200	10	models	model	NOUN
ajst-25954	200	11	have	have	VERB
ajst-25954	200	12	greater	great	ADJ
ajst-25954	200	13	fluctuation	fluctuation	NOUN
ajst-25954	200	14	during	during	ADP
ajst-25954	200	15	training	training	NOUN
ajst-25954	200	16	,	,	PUNCT
ajst-25954	200	17	while	while	SCONJ
ajst-25954	200	18	the	the	DET
ajst-25954	200	19	proposed	propose	VERB
ajst-25954	200	20	ms	ms	PROPN
ajst-25954	200	21	-	-	PUNCT
ajst-25954	200	22	stfm	stfm	NOUN
ajst-25954	200	23	-	-	PUNCT
ajst-25954	200	24	pcfwnet	pcfwnet	NOUN
ajst-25954	200	25	model	model	NOUN
ajst-25954	200	26	has	have	VERB
ajst-25954	200	27	stable	stable	ADJ
ajst-25954	200	28	convergence	convergence	NOUN
ajst-25954	200	29	and	and	CCONJ
ajst-25954	200	30	no	no	DET
ajst-25954	200	31	greater	great	ADJ
ajst-25954	200	32	fluctuation	fluctuation	NOUN
ajst-25954	200	33	.	.	PUNCT
ajst-25954	201	1	table	table	NOUN
ajst-25954	201	2	iv	iv	PROPN
ajst-25954	201	3	.	.	PUNCT
ajst-25954	202	1	the	the	DET
ajst-25954	202	2	proposed	propose	VERB
ajst-25954	202	3	method	method	NOUN
ajst-25954	202	4	is	be	AUX
ajst-25954	202	5	compared	compare	VERB
ajst-25954	202	6	with	with	ADP
ajst-25954	202	7	the	the	DET
ajst-25954	202	8	network	network	NOUN
ajst-25954	202	9	structure	structure	NOUN
ajst-25954	202	10	of	of	ADP
ajst-25954	202	11	four	four	NUM
ajst-25954	202	12	groups	group	NOUN
ajst-25954	202	13	of	of	ADP
ajst-25954	202	14	simplified	simplified	ADJ
ajst-25954	202	15	models	model	NOUN
ajst-25954	202	16	fm	fm	PROPN
ajst-25954	202	17	eca	eca	NOUN
ajst-25954	202	18	layer	layer	NOUN
ajst-25954	202	19	name	name	NOUN
ajst-25954	202	20	network	network	NOUN
ajst-25954	202	21	structure	structure	NOUN
ajst-25954	202	22	pcnn	pcnn	PROPN
ajst-25954	202	23	--	--	PUNCT
ajst-25954	202	24	input	input	NOUN
ajst-25954	202	25	input	input	NOUN
ajst-25954	202	26	layer	layer	NOUN
ajst-25954	202	27	inception1	inception1	NOUN
ajst-25954	203	1	inception2	inception2	NOUN
ajst-25954	203	2	inception3	inception3	NOUN
ajst-25954	203	3	concat	concat	PROPN
ajst-25954	203	4	layer	layer	NOUN
ajst-25954	203	5	mlp	mlp	PROPN
ajst-25954	203	6	layer	layer	NOUN
ajst-25954	203	7	softmax	softmax	NOUN
ajst-25954	203	8	9×9×6	9×9×6	NUM
ajst-25954	203	9	1	1	NUM
ajst-25954	203	10	1conv	1conv	NUM
ajst-25954	203	11	,	,	PUNCT
ajst-25954	203	12	32	32	NUM
ajst-25954	203	13	5	5	NUM
ajst-25954	203	14	5conv	5conv	NUM
ajst-25954	203	15	,	,	PUNCT
ajst-25954	203	16	128	128	NUM
ajst-25954	203	17	3	3	NUM
ajst-25954	203	18	3conv	3conv	NUM
ajst-25954	203	19	,	,	PUNCT
ajst-25954	203	20	128	128	NUM
ajst-25954	203	21	3	3	NUM
ajst-25954	203	22	3conv	3conv	NUM
ajst-25954	203	23	,	,	PUNCT
ajst-25954	203	24	128	128	NUM
ajst-25954	203	25	5	5	NUM
ajst-25954	203	26	5conv	5conv	NUM
ajst-25954	203	27	,	,	PUNCT
ajst-25954	203	28	128	128	NUM
ajst-25954	203	29	5	5	NUM
ajst-25954	203	30	5conv	5conv	NUM
ajst-25954	203	31	,	,	PUNCT
ajst-25954	203	32	128	128	NUM
ajst-25954	203	33	7	7	NUM
ajst-25954	203	34	7conv	7conv	NUM
ajst-25954	203	35	,	,	PUNCT
ajst-25954	203	36	128	128	NUM
ajst-25954	203	37	7	7	NUM
ajst-25954	203	38	7conv	7conv	NUM
ajst-25954	203	39	,	,	PUNCT
ajst-25954	203	40	128	128	NUM
ajst-25954	203	41	1	1	NUM
ajst-25954	203	42	1conv	1conv	NUM
ajst-25954	203	43	,	,	PUNCT
ajst-25954	203	44	512	512	NUM
ajst-25954	203	45	5	5	NUM
ajst-25954	203	46	5conv	5conv	NUM
ajst-25954	203	47	,	,	PUNCT
ajst-25954	203	48	96	96	NUM
ajst-25954	203	49	hl1	hl1	PROPN
ajst-25954	203	50	,	,	PUNCT
ajst-25954	203	51	1024units	1024units	NUM
ajst-25954	203	52	hl2	hl2	NOUN
ajst-25954	203	53	,	,	PUNCT
ajst-25954	203	54	512units	512units	PROPN
ajst-25954	203	55	2	2	NUM
ajst-25954	203	56	pcnn	pcnn	NOUN
ajst-25954	203	57	-	-	PUNCT
ajst-25954	203	58	fmnet	fmnet	ADJ
ajst-25954	203	59	√	√	CCONJ
ajst-25954	203	60	-	-	PUNCT
ajst-25954	203	61	pcnn	pcnn	NOUN
ajst-25954	203	62	-	-	PUNCT
ajst-25954	203	63	ecanet	ecanet	NOUN
ajst-25954	203	64	-√	-√	PUNCT
ajst-25954	203	65	this	this	DET
ajst-25954	203	66	work	work	NOUN
ajst-25954	203	67	√	√	NOUN
ajst-25954	203	68	√	√	PROPN
ajst-25954	203	69	it	it	PRON
ajst-25954	203	70	can	can	AUX
ajst-25954	203	71	be	be	AUX
ajst-25954	203	72	seen	see	VERB
ajst-25954	203	73	from	from	ADP
ajst-25954	203	74	fig	fig	NOUN
ajst-25954	203	75	.	.	PUNCT
ajst-25954	204	1	8	8	NUM
ajst-25954	204	2	and	and	CCONJ
ajst-25954	204	3	fig	fig	NOUN
ajst-25954	204	4	.	.	PUNCT
ajst-25954	205	1	9	9	NUM
ajst-25954	205	2	that	that	SCONJ
ajst-25954	205	3	the	the	DET
ajst-25954	205	4	proposed	propose	VERB
ajst-25954	205	5	msstfm	msstfm	NOUN
ajst-25954	205	6	-	-	PUNCT
ajst-25954	205	7	pcfwnet	pcfwnet	NOUN
ajst-25954	205	8	has	have	VERB
ajst-25954	205	9	more	more	ADV
ajst-25954	205	10	stable	stable	ADJ
ajst-25954	205	11	performance	performance	NOUN
ajst-25954	205	12	than	than	ADP
ajst-25954	205	13	pcnn	pcnn	NOUN
ajst-25954	205	14	,	,	PUNCT
ajst-25954	205	15	pcnn	pcnn	NOUN
ajst-25954	205	16	-	-	PUNCT
ajst-25954	205	17	fmnet	fmnet	PROPN
ajst-25954	205	18	and	and	CCONJ
ajst-25954	205	19	pcnn	pcnn	NOUN
ajst-25954	205	20	-	-	PUNCT
ajst-25954	205	21	ecanet	ecanet	NOUN
ajst-25954	205	22	.	.	PUNCT
ajst-25954	206	1	it	it	PRON
ajst-25954	206	2	can	can	AUX
ajst-25954	206	3	be	be	AUX
ajst-25954	206	4	concluded	conclude	VERB
ajst-25954	206	5	that	that	SCONJ
ajst-25954	206	6	the	the	DET
ajst-25954	206	7	integration	integration	NOUN
ajst-25954	206	8	of	of	ADP
ajst-25954	206	9	fm	fm	PROPN
ajst-25954	206	10	and	and	CCONJ
ajst-25954	206	11	eca	eca	PROPN
ajst-25954	206	12	modules	module	NOUN
ajst-25954	206	13	can	can	AUX
ajst-25954	206	14	better	well	ADV
ajst-25954	206	15	mine	mine	VERB
ajst-25954	206	16	the	the	DET
ajst-25954	206	17	89	89	NUM
ajst-25954	206	18	global	global	ADJ
ajst-25954	206	19	features	feature	NOUN
ajst-25954	206	20	and	and	CCONJ
ajst-25954	206	21	interactive	interactive	ADJ
ajst-25954	206	22	information	information	NOUN
ajst-25954	206	23	between	between	ADP
ajst-25954	206	24	features	feature	NOUN
ajst-25954	206	25	,	,	PUNCT
ajst-25954	206	26	which	which	PRON
ajst-25954	206	27	map	map	VERB
ajst-25954	206	28	the	the	DET
ajst-25954	206	29	discriminant	discriminant	ADJ
ajst-25954	206	30	representation	representation	NOUN
ajst-25954	206	31	of	of	ADP
ajst-25954	206	32	seizure	seizure	NOUN
ajst-25954	206	33	prediction	prediction	NOUN
ajst-25954	206	34	.	.	PUNCT
ajst-25954	207	1	thus	thus	ADV
ajst-25954	207	2	,	,	PUNCT
ajst-25954	207	3	the	the	DET
ajst-25954	207	4	learning	learning	NOUN
ajst-25954	207	5	ability	ability	NOUN
ajst-25954	207	6	is	be	AUX
ajst-25954	207	7	better	well	ADJ
ajst-25954	207	8	than	than	ADP
ajst-25954	207	9	that	that	PRON
ajst-25954	207	10	of	of	ADP
ajst-25954	207	11	the	the	DET
ajst-25954	207	12	three	three	NUM
ajst-25954	207	13	simplified	simplified	ADJ
ajst-25954	207	14	models	model	NOUN
ajst-25954	207	15	train	train	VERB
ajst-25954	207	16	validation	validation	NOUN
ajst-25954	207	17	figure	figure	NOUN
ajst-25954	207	18	8	8	NUM
ajst-25954	207	19	.	.	PUNCT
ajst-25954	208	1	the	the	DET
ajst-25954	208	2	change	change	NOUN
ajst-25954	208	3	process	process	NOUN
ajst-25954	208	4	of	of	ADP
ajst-25954	208	5	the	the	DET
ajst-25954	208	6	accuracy	accuracy	NOUN
ajst-25954	208	7	of	of	ADP
ajst-25954	208	8	the	the	DET
ajst-25954	208	9	first	first	ADJ
ajst-25954	208	10	300	300	NUM
ajst-25954	208	11	training	training	NOUN
ajst-25954	208	12	cycles	cycle	NOUN
ajst-25954	208	13	of	of	ADP
ajst-25954	208	14	the	the	DET
ajst-25954	208	15	training	training	NOUN
ajst-25954	208	16	set	set	NOUN
ajst-25954	208	17	and	and	CCONJ
ajst-25954	208	18	the	the	DET
ajst-25954	208	19	validation	validation	NOUN
ajst-25954	208	20	set	set	VERB
ajst-25954	208	21	on	on	ADP
ajst-25954	208	22	the	the	DET
ajst-25954	208	23	chb	chb	NOUN
ajst-25954	208	24	-	-	PUNCT
ajst-25954	208	25	mit	mit	NOUN
ajst-25954	208	26	dataset	dataset	NOUN
ajst-25954	208	27	.	.	PUNCT
ajst-25954	209	1	table	table	NOUN
ajst-25954	209	2	v.	v.	ADP
ajst-25954	209	3	the	the	DET
ajst-25954	209	4	experimental	experimental	ADJ
ajst-25954	209	5	method	method	NOUN
ajst-25954	209	6	and	and	CCONJ
ajst-25954	209	7	performance	performance	NOUN
ajst-25954	209	8	comparison	comparison	NOUN
ajst-25954	209	9	between	between	ADP
ajst-25954	209	10	the	the	DET
ajst-25954	209	11	proposed	propose	VERB
ajst-25954	209	12	method	method	NOUN
ajst-25954	209	13	and	and	CCONJ
ajst-25954	209	14	the	the	DET
ajst-25954	209	15	advanced	advanced	ADJ
ajst-25954	209	16	method	method	NOUN
ajst-25954	209	17	on	on	ADP
ajst-25954	209	18	the	the	DET
ajst-25954	209	19	chb	chb	NOUN
ajst-25954	209	20	-	-	PUNCT
ajst-25954	209	21	mit	mit	NOUN
ajst-25954	209	22	dataset	dataset	NOUN
ajst-25954	209	23	are	be	AUX
ajst-25954	209	24	presented	present	VERB
ajst-25954	209	25	author	author	NOUN
ajst-25954	209	26	year	year	NOUN
ajst-25954	209	27	method	method	NOUN
ajst-25954	209	28	acc(%	acc(%	PROPN
ajst-25954	209	29	)	)	PUNCT
ajst-25954	209	30	sen(%	sen(%	PROPN
ajst-25954	209	31	)	)	PUNCT
ajst-25954	209	32	spe(%	spe(%	PROPN
ajst-25954	209	33	)	)	PUNCT
ajst-25954	209	34	wei	wei	PROPN
ajst-25954	209	35	et	et	PROPN
ajst-25954	209	36	al.[8	al.[8	PROPN
ajst-25954	209	37	]	]	PUNCT
ajst-25954	209	38	2019	2019	NUM
ajst-25954	209	39	lrcn	lrcn	PROPN
ajst-25954	209	40	93.4	93.4	NUM
ajst-25954	209	41	91.8	91.8	NUM
ajst-25954	209	42	86.1	86.1	NUM
ajst-25954	209	43	yu	yu	PROPN
ajst-25954	209	44	et	et	PROPN
ajst-25954	209	45	al.[11	al.[11	PROPN
ajst-25954	209	46	]	]	PUNCT
ajst-25954	209	47	2022	2022	NUM
ajst-25954	209	48	mlstm	mlstm	NOUN
ajst-25954	209	49	-89.5	-89.5	PROPN
ajst-25954	209	50	-	-	PUNCT
ajst-25954	209	51	sun	sun	PROPN
ajst-25954	209	52	et	et	PROPN
ajst-25954	209	53	al.[16	al.[16	PROPN
ajst-25954	209	54	]	]	PUNCT
ajst-25954	209	55	2021	2021	NUM
ajst-25954	209	56	cadcnn	cadcnn	NOUN
ajst-25954	210	1	-97.1	-97.1	NUM
ajst-25954	210	2	95.6	95.6	NUM
ajst-25954	210	3	gao	gao	PROPN
ajst-25954	210	4	et	et	PROPN
ajst-25954	210	5	al.[26	al.[26	PROPN
ajst-25954	210	6	]	]	PUNCT
ajst-25954	210	7	2020	2020	NUM
ajst-25954	211	1	psded+dcnn	psded+dcnn	PROPN
ajst-25954	211	2	90.0	90.0	NUM
ajst-25954	211	3	--	--	PUNCT
ajst-25954	211	4	ma	ma	PROPN
ajst-25954	211	5	et	et	PROPN
ajst-25954	211	6	al.[31	al.[31	PROPN
ajst-25954	211	7	]	]	PUNCT
ajst-25954	211	8	2023	2023	NUM
ajst-25954	211	9	cnn	cnn	PROPN
ajst-25954	211	10	-	-	PUNCT
ajst-25954	211	11	bi	bi	NOUN
ajst-25954	211	12	-	-	ADJ
ajst-25954	211	13	lstm	lstm	ADJ
ajst-25954	211	14	94.8	94.8	NUM
ajst-25954	211	15	94.8	94.8	NUM
ajst-25954	211	16	94.8	94.8	NUM
ajst-25954	211	17	zhong	zhong	PROPN
ajst-25954	211	18	et	et	PROPN
ajst-25954	211	19	al.[32	al.[32	PROPN
ajst-25954	211	20	]	]	PUNCT
ajst-25954	211	21	2023	2023	NUM
ajst-25954	211	22	svm	svm	NOUN
ajst-25954	211	23	95.9	95.9	NUM
ajst-25954	211	24	-94.9	-94.9	NUM
ajst-25954	211	25	ma	ma	PROPN
ajst-25954	211	26	et	et	PROPN
ajst-25954	211	27	al.[33	al.[33	PROPN
ajst-25954	211	28	]	]	PUNCT
ajst-25954	211	29	2021	2021	NUM
ajst-25954	212	1	bnlstm+casa	bnlstm+casa	PROPN
ajst-25954	212	2	95.6	95.6	NUM
ajst-25954	212	3	96.2	96.2	NUM
ajst-25954	212	4	91.5	91.5	NUM
ajst-25954	212	5	yang	yang	PROPN
ajst-25954	212	6	et	et	PROPN
ajst-25954	212	7	al.[34	al.[34	PROPN
ajst-25954	212	8	]	]	PUNCT
ajst-25954	212	9	2021	2021	NUM
ajst-25954	212	10	stft+rdanet	stft+rdanet	PROPN
ajst-25954	212	11	92.0	92.0	NUM
ajst-25954	212	12	-92.7	-92.7	NUM
ajst-25954	212	13	s	s	NOUN
ajst-25954	212	14	,	,	PUNCT
ajst-25954	212	15	m	m	VERB
ajst-25954	212	16	et	et	NOUN
ajst-25954	212	17	al.[35	al.[35	PROPN
ajst-25954	212	18	]	]	PUNCT
ajst-25954	212	19	2020	2020	NUM
ajst-25954	212	20	stft+cnn+svm	stft+cnn+svm	X
ajst-25954	212	21	-92.7	-92.7	NUM
ajst-25954	212	22	90.8	90.8	NUM
ajst-25954	212	23	this	this	DET
ajst-25954	212	24	work	work	NOUN
ajst-25954	212	25	2024	2024	NUM
ajst-25954	212	26	ms	ms	PROPN
ajst-25954	212	27	-	-	PUNCT
ajst-25954	212	28	stfm	stfm	NOUN
ajst-25954	212	29	-	-	PUNCT
ajst-25954	212	30	pcfwnet	pcfwnet	NOUN
ajst-25954	212	31	96.8	96.8	NUM
ajst-25954	212	32	96.9	96.9	NUM
ajst-25954	212	33	97.2	97.2	NUM
ajst-25954	212	34	table	table	NOUN
ajst-25954	212	35	vi	vi	NOUN
ajst-25954	212	36	.	.	PUNCT
ajst-25954	213	1	the	the	DET
ajst-25954	213	2	experimental	experimental	ADJ
ajst-25954	213	3	method	method	NOUN
ajst-25954	213	4	and	and	CCONJ
ajst-25954	213	5	performance	performance	NOUN
ajst-25954	213	6	comparison	comparison	NOUN
ajst-25954	213	7	between	between	ADP
ajst-25954	213	8	the	the	DET
ajst-25954	213	9	proposed	propose	VERB
ajst-25954	213	10	method	method	NOUN
ajst-25954	213	11	and	and	CCONJ
ajst-25954	213	12	the	the	DET
ajst-25954	213	13	advanced	advanced	ADJ
ajst-25954	213	14	method	method	NOUN
ajst-25954	213	15	on	on	ADP
ajst-25954	213	16	the	the	DET
ajst-25954	213	17	bonn	bonn	PROPN
ajst-25954	213	18	dataset	dataset	NOUN
ajst-25954	213	19	are	be	AUX
ajst-25954	213	20	presented	present	VERB
ajst-25954	213	21	author	author	NOUN
ajst-25954	213	22	year	year	NOUN
ajst-25954	213	23	method	method	NOUN
ajst-25954	213	24	acc(%	acc(%	PROPN
ajst-25954	213	25	)	)	PUNCT
ajst-25954	213	26	sen(%	sen(%	PROPN
ajst-25954	213	27	)	)	PUNCT
ajst-25954	213	28	spe(%	spe(%	PROPN
ajst-25954	213	29	)	)	PUNCT
ajst-25954	213	30	turk	turk	PROPN
ajst-25954	213	31	et	et	PROPN
ajst-25954	213	32	al.[36	al.[36	PROPN
ajst-25954	213	33	]	]	PUNCT
ajst-25954	213	34	2019	2019	NUM
ajst-25954	213	35	cwt	cwt	NOUN
ajst-25954	213	36	and	and	CCONJ
ajst-25954	213	37	cnn	cnn	PROPN
ajst-25954	213	38	98.5	98.5	NUM
ajst-25954	213	39	98.00	98.00	NUM
ajst-25954	213	40	98.98	98.98	NUM
ajst-25954	213	41	chakraborty	chakraborty	NOUN
ajst-25954	213	42	et	et	NOUN
ajst-25954	213	43	al.[37	al.[37	NOUN
ajst-25954	213	44	]	]	PUNCT
ajst-25954	213	45	2021	2021	NUM
ajst-25954	213	46	mssfs	mssfs	NOUN
ajst-25954	213	47	and	and	CCONJ
ajst-25954	213	48	rf	rf	NOUN
ajst-25954	213	49	classifier	classifier	NOUN
ajst-25954	213	50	99.65	99.65	NUM
ajst-25954	213	51	98.06	98.06	NUM
ajst-25954	213	52	100	100	NUM
ajst-25954	213	53	zhao	zhao	PROPN
ajst-25954	213	54	et	et	NOUN
ajst-25954	213	55	al.[38	al.[38	PROPN
ajst-25954	213	56	]	]	PUNCT
ajst-25954	213	57	2020	2020	NUM
ajst-25954	213	58	1d	1d	NUM
ajst-25954	213	59	-	-	PUNCT
ajst-25954	213	60	cnn	cnn	PROPN
ajst-25954	213	61	97.63	97.63	NUM
ajst-25954	213	62	--	--	PUNCT
ajst-25954	213	63	mahfuz	mahfuz	NOUN
ajst-25954	213	64	et	et	NOUN
ajst-25954	213	65	al.[39	al.[39	PROPN
ajst-25954	213	66	]	]	X
ajst-25954	213	67	2021	2021	NUM
ajst-25954	214	1	deep	deep	ADJ
ajst-25954	214	2	cnn	cnn	PROPN
ajst-25954	214	3	and	and	CCONJ
ajst-25954	214	4	cwt	cwt	PROPN
ajst-25954	214	5	98.44	98.44	NUM
ajst-25954	214	6	97.50	97.50	NUM
ajst-25954	214	7	98.38	98.38	NUM
ajst-25954	214	8	this	this	DET
ajst-25954	214	9	work	work	NOUN
ajst-25954	214	10	2024	2024	NUM
ajst-25954	214	11	ms	ms	PROPN
ajst-25954	214	12	-	-	PUNCT
ajst-25954	214	13	stfm	stfm	NOUN
ajst-25954	214	14	-	-	PUNCT
ajst-25954	214	15	pcfwnet	pcfwnet	NOUN
ajst-25954	214	16	100	100	NUM
ajst-25954	214	17	100	100	NUM
ajst-25954	214	18	100	100	NUM
ajst-25954	214	19	1	1	NUM
ajst-25954	214	20	.	.	PUNCT
ajst-25954	215	1	comparative	comparative	ADJ
ajst-25954	215	2	analysis	analysis	NOUN
ajst-25954	215	3	with	with	ADP
ajst-25954	215	4	previous	previous	ADJ
ajst-25954	215	5	methods	method	NOUN
ajst-25954	215	6	in	in	ADP
ajst-25954	215	7	order	order	NOUN
ajst-25954	215	8	to	to	PART
ajst-25954	215	9	demonstrate	demonstrate	VERB
ajst-25954	215	10	the	the	DET
ajst-25954	215	11	advantages	advantage	NOUN
ajst-25954	215	12	of	of	ADP
ajst-25954	215	13	the	the	DET
ajst-25954	215	14	proposed	propose	VERB
ajst-25954	215	15	model	model	NOUN
ajst-25954	215	16	,	,	PUNCT
ajst-25954	215	17	the	the	DET
ajst-25954	215	18	performance	performance	NOUN
ajst-25954	215	19	of	of	ADP
ajst-25954	215	20	the	the	DET
ajst-25954	215	21	previous	previous	ADJ
ajst-25954	215	22	seizure	seizure	NOUN
ajst-25954	215	23	prediction	prediction	NOUN
ajst-25954	215	24	90	90	NUM
ajst-25954	215	25	methods	method	NOUN
ajst-25954	215	26	on	on	ADP
ajst-25954	215	27	the	the	DET
ajst-25954	215	28	chb	chb	NOUN
ajst-25954	215	29	-	-	PUNCT
ajst-25954	215	30	mit	mit	NOUN
ajst-25954	215	31	dataset	dataset	NOUN
ajst-25954	215	32	is	be	AUX
ajst-25954	215	33	summarized	summarize	VERB
ajst-25954	215	34	,	,	PUNCT
ajst-25954	215	35	which	which	PRON
ajst-25954	215	36	the	the	DET
ajst-25954	215	37	objective	objective	ADJ
ajst-25954	215	38	comparative	comparative	ADJ
ajst-25954	215	39	analysis	analysis	NOUN
ajst-25954	215	40	of	of	ADP
ajst-25954	215	41	these	these	DET
ajst-25954	215	42	methods	method	NOUN
ajst-25954	215	43	is	be	AUX
ajst-25954	215	44	carried	carry	VERB
ajst-25954	215	45	out	out	ADP
ajst-25954	215	46	.	.	PUNCT
ajst-25954	216	1	figure	figure	NOUN
ajst-25954	216	2	9	9	NUM
ajst-25954	216	3	.	.	PUNCT
ajst-25954	216	4	confusion	confusion	NOUN
ajst-25954	216	5	matrix	matrix	NOUN
ajst-25954	216	6	of	of	ADP
ajst-25954	216	7	four	four	NUM
ajst-25954	216	8	network	network	NOUN
ajst-25954	216	9	models	model	NOUN
ajst-25954	216	10	on	on	ADP
ajst-25954	216	11	chb	chb	NOUN
ajst-25954	216	12	-	-	PUNCT
ajst-25954	216	13	mit	mit	NOUN
ajst-25954	216	14	and	and	CCONJ
ajst-25954	216	15	bonn	bonn	PROPN
ajst-25954	216	16	datasets	dataset	NOUN
ajst-25954	216	17	.	.	PUNCT
ajst-25954	217	1	as	as	SCONJ
ajst-25954	217	2	shown	show	VERB
ajst-25954	217	3	in	in	ADP
ajst-25954	217	4	table	table	NOUN
ajst-25954	217	5	v	v	ADP
ajst-25954	217	6	and	and	CCONJ
ajst-25954	217	7	table	table	NOUN
ajst-25954	217	8	vi	vi	PROPN
ajst-25954	217	9	,	,	PUNCT
ajst-25954	217	10	it	it	PRON
ajst-25954	217	11	can	can	AUX
ajst-25954	217	12	be	be	AUX
ajst-25954	217	13	seen	see	VERB
ajst-25954	217	14	from	from	ADP
ajst-25954	217	15	the	the	DET
ajst-25954	217	16	table	table	NOUN
ajst-25954	217	17	that	that	SCONJ
ajst-25954	217	18	both	both	PRON
ajst-25954	218	1	[	[	X
ajst-25954	218	2	34	34	NUM
ajst-25954	218	3	]	]	PUNCT
ajst-25954	218	4	and	and	CCONJ
ajst-25954	218	5	[	[	X
ajst-25954	218	6	35	35	NUM
ajst-25954	218	7	]	]	PUNCT
ajst-25954	218	8	use	use	VERB
ajst-25954	218	9	short	short	ADJ
ajst-25954	218	10	-	-	PUNCT
ajst-25954	218	11	time	time	NOUN
ajst-25954	218	12	fourier	fourier	NOUN
ajst-25954	218	13	transform	transform	NOUN
ajst-25954	218	14	(	(	PUNCT
ajst-25954	218	15	stft	stft	NOUN
ajst-25954	218	16	)	)	PUNCT
ajst-25954	218	17	to	to	PART
ajst-25954	218	18	extract	extract	VERB
ajst-25954	218	19	features	feature	NOUN
ajst-25954	218	20	,	,	PUNCT
ajst-25954	218	21	and	and	CCONJ
ajst-25954	218	22	the	the	DET
ajst-25954	218	23	specificity	specificity	NOUN
ajst-25954	218	24	is	be	AUX
ajst-25954	218	25	92.7	92.7	NUM
ajst-25954	218	26	%	%	NOUN
ajst-25954	218	27	and	and	CCONJ
ajst-25954	218	28	90.8	90.8	NUM
ajst-25954	218	29	%	%	NOUN
ajst-25954	218	30	,	,	PUNCT
ajst-25954	218	31	which	which	PRON
ajst-25954	218	32	is	be	AUX
ajst-25954	218	33	lower	low	ADJ
ajst-25954	218	34	than	than	ADP
ajst-25954	218	35	the	the	DET
ajst-25954	218	36	specificity	specificity	NOUN
ajst-25954	218	37	of	of	ADP
ajst-25954	218	38	the	the	DET
ajst-25954	218	39	proposed	propose	VERB
ajst-25954	218	40	method	method	NOUN
ajst-25954	218	41	.	.	PUNCT
ajst-25954	219	1	this	this	PRON
ajst-25954	219	2	is	be	AUX
ajst-25954	219	3	attributed	attribute	VERB
ajst-25954	219	4	to	to	ADP
ajst-25954	219	5	the	the	DET
ajst-25954	219	6	proposed	propose	VERB
ajst-25954	219	7	ms	ms	NOUN
ajst-25954	219	8	feature	feature	NOUN
ajst-25954	219	9	extractor	extractor	NOUN
ajst-25954	219	10	can	can	AUX
ajst-25954	219	11	extract	extract	VERB
ajst-25954	219	12	multi	multi	ADJ
ajst-25954	219	13	-	-	ADJ
ajst-25954	219	14	scale	scale	ADJ
ajst-25954	219	15	spatiotemporal	spatiotemporal	ADJ
ajst-25954	219	16	features	feature	NOUN
ajst-25954	219	17	of	of	ADP
ajst-25954	219	18	electrode	electrode	NOUN
ajst-25954	219	19	channels	channel	NOUN
ajst-25954	219	20	,	,	PUNCT
ajst-25954	219	21	which	which	PRON
ajst-25954	219	22	capture	capture	VERB
ajst-25954	219	23	gain	gain	VERB
ajst-25954	219	24	information	information	NOUN
ajst-25954	219	25	.	.	PUNCT
ajst-25954	220	1	compared	compare	VERB
ajst-25954	220	2	with	with	ADP
ajst-25954	220	3	[	[	X
ajst-25954	220	4	26	26	NUM
ajst-25954	220	5	]	]	PUNCT
ajst-25954	220	6	,	,	PUNCT
ajst-25954	220	7	[	[	X
ajst-25954	220	8	31	31	NUM
ajst-25954	220	9	]	]	PUNCT
ajst-25954	220	10	and	and	CCONJ
ajst-25954	220	11	[	[	X
ajst-25954	220	12	32	32	NUM
ajst-25954	220	13	]	]	PUNCT
ajst-25954	220	14	,	,	PUNCT
ajst-25954	220	15	the	the	DET
ajst-25954	220	16	proposed	propose	VERB
ajst-25954	220	17	pcfwnet	pcfwnet	NOUN
ajst-25954	220	18	method	method	NOUN
ajst-25954	220	19	can	can	AUX
ajst-25954	220	20	learn	learn	VERB
ajst-25954	220	21	spatiotemporal	spatiotemporal	ADJ
ajst-25954	220	22	features	feature	NOUN
ajst-25954	220	23	more	more	ADV
ajst-25954	220	24	efficiently	efficiently	ADV
ajst-25954	220	25	,	,	PUNCT
ajst-25954	220	26	and	and	CCONJ
ajst-25954	220	27	thus	thus	ADV
ajst-25954	220	28	the	the	DET
ajst-25954	220	29	accuracy	accuracy	NOUN
ajst-25954	220	30	of	of	ADP
ajst-25954	220	31	the	the	DET
ajst-25954	220	32	three	three	NUM
ajst-25954	220	33	previous	previous	ADJ
ajst-25954	220	34	methods	method	NOUN
ajst-25954	220	35	is	be	AUX
ajst-25954	220	36	improved	improve	VERB
ajst-25954	220	37	by	by	ADP
ajst-25954	220	38	6.8	6.8	NUM
ajst-25954	220	39	%	%	NOUN
ajst-25954	220	40	,	,	PUNCT
ajst-25954	220	41	2.0	2.0	NUM
ajst-25954	220	42	%	%	NOUN
ajst-25954	220	43	and	and	CCONJ
ajst-25954	220	44	0.8	0.8	NUM
ajst-25954	220	45	%	%	NOUN
ajst-25954	220	46	.	.	PUNCT
ajst-25954	221	1	for	for	ADP
ajst-25954	221	2	bonn	bonn	PROPN
ajst-25954	221	3	dataset	dataset	NOUN
ajst-25954	221	4	,	,	PUNCT
ajst-25954	221	5	the	the	DET
ajst-25954	221	6	proposed	propose	VERB
ajst-25954	221	7	method	method	NOUN
ajst-25954	221	8	is	be	AUX
ajst-25954	221	9	also	also	ADV
ajst-25954	221	10	superior	superior	ADJ
ajst-25954	221	11	to	to	ADP
ajst-25954	221	12	previous	previous	ADJ
ajst-25954	221	13	methods	method	NOUN
ajst-25954	221	14	.	.	PUNCT
ajst-25954	222	1	therefore	therefore	ADV
ajst-25954	222	2	,	,	PUNCT
ajst-25954	222	3	compared	compare	VERB
ajst-25954	222	4	with	with	ADP
ajst-25954	222	5	other	other	ADJ
ajst-25954	222	6	previous	previous	ADJ
ajst-25954	222	7	methods	method	NOUN
ajst-25954	222	8	,	,	PUNCT
ajst-25954	222	9	the	the	DET
ajst-25954	222	10	msstfm	msstfm	NOUN
ajst-25954	222	11	-	-	PUNCT
ajst-25954	222	12	pcfwnet	pcfwnet	NOUN
ajst-25954	222	13	model	model	NOUN
ajst-25954	222	14	proposed	propose	VERB
ajst-25954	222	15	can	can	AUX
ajst-25954	222	16	effectively	effectively	ADV
ajst-25954	222	17	capture	capture	VERB
ajst-25954	222	18	the	the	DET
ajst-25954	222	19	global	global	ADJ
ajst-25954	222	20	features	feature	NOUN
ajst-25954	222	21	and	and	CCONJ
ajst-25954	222	22	interaction	interaction	NOUN
ajst-25954	222	23	information	information	NOUN
ajst-25954	222	24	between	between	ADP
ajst-25954	222	25	spatiotemporal	spatiotemporal	ADJ
ajst-25954	222	26	features	feature	NOUN
ajst-25954	222	27	in	in	ADP
ajst-25954	222	28	this	this	DET
ajst-25954	222	29	paper	paper	NOUN
ajst-25954	222	30	.	.	PUNCT
ajst-25954	223	1	in	in	ADP
ajst-25954	223	2	general	general	ADJ
ajst-25954	223	3	,	,	PUNCT
ajst-25954	223	4	the	the	DET
ajst-25954	223	5	proposed	propose	VERB
ajst-25954	223	6	method	method	NOUN
ajst-25954	223	7	can	can	AUX
ajst-25954	223	8	compensate	compensate	VERB
ajst-25954	223	9	for	for	ADP
ajst-25954	223	10	the	the	DET
ajst-25954	223	11	spatiotemporal	spatiotemporal	ADJ
ajst-25954	223	12	feature	feature	NOUN
ajst-25954	223	13	information	information	NOUN
ajst-25954	223	14	guided	guide	VERB
ajst-25954	223	15	by	by	ADP
ajst-25954	223	16	multi	multi	ADJ
ajst-25954	223	17	-	-	ADJ
ajst-25954	223	18	channel	channel	ADJ
ajst-25954	223	19	spatial	spatial	ADJ
ajst-25954	223	20	location	location	NOUN
ajst-25954	223	21	in	in	ADP
ajst-25954	223	22	brain	brain	NOUN
ajst-25954	223	23	regions	region	NOUN
ajst-25954	223	24	,	,	PUNCT
ajst-25954	223	25	at	at	ADP
ajst-25954	223	26	the	the	DET
ajst-25954	223	27	same	same	ADJ
ajst-25954	223	28	time	time	NOUN
ajst-25954	223	29	,	,	PUNCT
ajst-25954	223	30	the	the	DET
ajst-25954	223	31	interaction	interaction	NOUN
ajst-25954	223	32	information	information	NOUN
ajst-25954	223	33	between	between	ADP
ajst-25954	223	34	learning	learn	VERB
ajst-25954	223	35	features	feature	NOUN
ajst-25954	223	36	can	can	AUX
ajst-25954	223	37	be	be	AUX
ajst-25954	223	38	captured	capture	VERB
ajst-25954	223	39	by	by	ADP
ajst-25954	223	40	the	the	DET
ajst-25954	223	41	parallel	parallel	ADJ
ajst-25954	223	42	channel	channel	NOUN
ajst-25954	223	43	attention	attention	NOUN
ajst-25954	223	44	.	.	PUNCT
ajst-25954	224	1	5	5	X
ajst-25954	224	2	.	.	X
ajst-25954	224	3	conclusion	conclusion	NOUN
ajst-25954	224	4	in	in	ADP
ajst-25954	224	5	this	this	DET
ajst-25954	224	6	paper	paper	NOUN
ajst-25954	224	7	,	,	PUNCT
ajst-25954	224	8	the	the	DET
ajst-25954	224	9	ms	ms	PROPN
ajst-25954	224	10	-	-	PUNCT
ajst-25954	224	11	stfm	stfm	NOUN
ajst-25954	224	12	-	-	PUNCT
ajst-25954	224	13	pcfwnet	pcfwnet	NOUN
ajst-25954	224	14	model	model	NOUN
ajst-25954	224	15	for	for	ADP
ajst-25954	224	16	seizure	seizure	NOUN
ajst-25954	224	17	prediction	prediction	NOUN
ajst-25954	224	18	is	be	AUX
ajst-25954	224	19	proposed	propose	VERB
ajst-25954	224	20	.	.	PUNCT
ajst-25954	225	1	multi	multi	ADJ
ajst-25954	225	2	-	-	ADJ
ajst-25954	225	3	channel	channel	ADJ
ajst-25954	225	4	spatiotemporal	spatiotemporal	ADJ
ajst-25954	225	5	features	feature	NOUN
ajst-25954	225	6	are	be	AUX
ajst-25954	225	7	extracted	extract	VERB
ajst-25954	225	8	from	from	ADP
ajst-25954	225	9	eeg	eeg	NOUN
ajst-25954	225	10	signals	signal	NOUN
ajst-25954	225	11	by	by	ADP
ajst-25954	225	12	using	use	VERB
ajst-25954	225	13	3d	3d	NUM
ajst-25954	225	14	feature	feature	NOUN
ajst-25954	225	15	matrix	matrix	NOUN
ajst-25954	225	16	.	.	PUNCT
ajst-25954	226	1	convolution	convolution	NOUN
ajst-25954	226	2	operations	operation	NOUN
ajst-25954	226	3	are	be	AUX
ajst-25954	226	4	applied	apply	VERB
ajst-25954	226	5	to	to	ADP
ajst-25954	226	6	the	the	DET
ajst-25954	226	7	feature	feature	NOUN
ajst-25954	226	8	maps	map	NOUN
ajst-25954	226	9	using	use	VERB
ajst-25954	226	10	inception	inception	ADJ
ajst-25954	226	11	modules	module	NOUN
ajst-25954	226	12	of	of	ADP
ajst-25954	226	13	different	different	ADJ
ajst-25954	226	14	scales	scale	NOUN
ajst-25954	226	15	,	,	PUNCT
ajst-25954	226	16	and	and	CCONJ
ajst-25954	226	17	the	the	DET
ajst-25954	226	18	gain	gain	NOUN
ajst-25954	226	19	information	information	NOUN
ajst-25954	226	20	is	be	AUX
ajst-25954	226	21	extracted	extract	VERB
ajst-25954	226	22	by	by	ADP
ajst-25954	226	23	merging	merge	VERB
ajst-25954	226	24	in	in	ADP
ajst-25954	226	25	the	the	DET
ajst-25954	226	26	same	same	ADJ
ajst-25954	226	27	dimension	dimension	NOUN
ajst-25954	226	28	.	.	PUNCT
ajst-25954	227	1	the	the	DET
ajst-25954	227	2	fm	fm	PROPN
ajst-25954	227	3	layer	layer	NOUN
ajst-25954	227	4	is	be	AUX
ajst-25954	227	5	employed	employ	VERB
ajst-25954	227	6	to	to	PART
ajst-25954	227	7	merge	merge	VERB
ajst-25954	227	8	the	the	DET
ajst-25954	227	9	outputs	output	NOUN
ajst-25954	227	10	of	of	ADP
ajst-25954	227	11	three	three	NUM
ajst-25954	227	12	different	different	ADJ
ajst-25954	227	13	scale	scale	NOUN
ajst-25954	227	14	inceptionmodules	inceptionmodule	NOUN
ajst-25954	227	15	,	,	PUNCT
ajst-25954	227	16	for	for	ADP
ajst-25954	227	17	feature	feature	NOUN
ajst-25954	227	18	interaction	interaction	NOUN
ajst-25954	227	19	modeling	modeling	NOUN
ajst-25954	227	20	,	,	PUNCT
ajst-25954	227	21	which	which	PRON
ajst-25954	227	22	the	the	DET
ajst-25954	227	23	interaction	interaction	NOUN
ajst-25954	227	24	and	and	CCONJ
ajst-25954	227	25	correlation	correlation	NOUN
ajst-25954	227	26	information	information	NOUN
ajst-25954	227	27	between	between	ADP
ajst-25954	227	28	channel	channel	NOUN
ajst-25954	227	29	features	feature	NOUN
ajst-25954	227	30	are	be	AUX
ajst-25954	227	31	captured	capture	VERB
ajst-25954	227	32	.	.	PUNCT
ajst-25954	228	1	finally	finally	ADV
ajst-25954	228	2	,	,	PUNCT
ajst-25954	228	3	the	the	DET
ajst-25954	228	4	pcfwnet	pcfwnet	NOUN
ajst-25954	228	5	is	be	AUX
ajst-25954	228	6	utilized	utilize	VERB
ajst-25954	228	7	to	to	PART
ajst-25954	228	8	dynamically	dynamically	ADV
ajst-25954	228	9	adjust	adjust	VERB
ajst-25954	228	10	the	the	DET
ajst-25954	228	11	channel	channel	NOUN
ajst-25954	228	12	weights	weight	NOUN
ajst-25954	228	13	,	,	PUNCT
ajst-25954	228	14	which	which	PRON
ajst-25954	228	15	captures	capture	VERB
ajst-25954	228	16	the	the	DET
ajst-25954	228	17	global	global	ADJ
ajst-25954	228	18	features	feature	NOUN
ajst-25954	228	19	between	between	ADP
ajst-25954	228	20	the	the	DET
ajst-25954	228	21	fm	fm	PROPN
ajst-25954	228	22	and	and	CCONJ
ajst-25954	228	23	linear	linear	VERB
ajst-25954	228	24	hidden	hide	VERB
ajst-25954	228	25	layer	layer	NOUN
ajst-25954	228	26	outputs	output	NOUN
ajst-25954	228	27	.	.	PUNCT
ajst-25954	229	1	the	the	DET
ajst-25954	229	2	accuracy	accuracy	NOUN
ajst-25954	229	3	,	,	PUNCT
ajst-25954	229	4	sensitivity	sensitivity	NOUN
ajst-25954	229	5	and	and	CCONJ
ajst-25954	229	6	specificity	specificity	NOUN
ajst-25954	229	7	of	of	ADP
ajst-25954	229	8	the	the	DET
ajst-25954	229	9	method	method	NOUN
ajst-25954	229	10	are	be	AUX
ajst-25954	229	11	96.8	96.8	NUM
ajst-25954	229	12	%	%	NOUN
ajst-25954	229	13	,	,	PUNCT
ajst-25954	229	14	96.9	96.9	NUM
ajst-25954	229	15	%	%	NOUN
ajst-25954	229	16	and	and	CCONJ
ajst-25954	229	17	97.2	97.2	NUM
ajst-25954	229	18	%	%	NOUN
ajst-25954	229	19	.	.	PUNCT
ajst-25954	230	1	compared	compare	VERB
ajst-25954	230	2	with	with	ADP
ajst-25954	230	3	previous	previous	ADJ
ajst-25954	230	4	research	research	NOUN
ajst-25954	230	5	tasks	task	NOUN
ajst-25954	230	6	,	,	PUNCT
ajst-25954	230	7	the	the	DET
ajst-25954	230	8	experimental	experimental	ADJ
ajst-25954	230	9	results	result	NOUN
ajst-25954	230	10	show	show	VERB
ajst-25954	230	11	that	that	SCONJ
ajst-25954	230	12	the	the	DET
ajst-25954	230	13	method	method	NOUN
ajst-25954	230	14	has	have	VERB
ajst-25954	230	15	relatively	relatively	ADV
ajst-25954	230	16	high	high	ADJ
ajst-25954	230	17	accuracy	accuracy	NOUN
ajst-25954	230	18	.	.	PUNCT
ajst-25954	231	1	due	due	ADP
ajst-25954	231	2	to	to	ADP
ajst-25954	231	3	different	different	ADJ
ajst-25954	231	4	epileptic	epileptic	ADJ
ajst-25954	231	5	patients	patient	NOUN
ajst-25954	231	6	have	have	VERB
ajst-25954	231	7	different	different	ADJ
ajst-25954	231	8	epileptic	epileptic	ADJ
ajst-25954	231	9	eeg	eeg	NOUN
ajst-25954	231	10	data	datum	NOUN
ajst-25954	231	11	,	,	PUNCT
ajst-25954	231	12	more	more	ADJ
ajst-25954	231	13	subjects	subject	NOUN
ajst-25954	231	14	of	of	ADP
ajst-25954	231	15	different	different	ADJ
ajst-25954	231	16	age	age	NOUN
ajst-25954	231	17	groups	group	NOUN
ajst-25954	231	18	,	,	PUNCT
ajst-25954	231	19	clinical	clinical	ADJ
ajst-25954	231	20	conditions	condition	NOUN
ajst-25954	231	21	,	,	PUNCT
ajst-25954	231	22	which	which	DET
ajst-25954	231	23	disease	disease	NOUN
ajst-25954	231	24	characteristics	characteristic	NOUN
ajst-25954	231	25	need	need	VERB
ajst-25954	231	26	to	to	PART
ajst-25954	231	27	be	be	AUX
ajst-25954	231	28	tested	test	VERB
ajst-25954	231	29	in	in	ADP
ajst-25954	231	30	future	future	ADJ
ajst-25954	231	31	work	work	NOUN
ajst-25954	231	32	to	to	PART
ajst-25954	231	33	ensure	ensure	VERB
ajst-25954	231	34	the	the	DET
ajst-25954	231	35	popularization	popularization	NOUN
ajst-25954	231	36	of	of	ADP
ajst-25954	231	37	the	the	DET
ajst-25954	231	38	method	method	NOUN
ajst-25954	231	39	.	.	PUNCT
ajst-25954	232	1	as	as	SCONJ
ajst-25954	232	2	data	data	NOUN
ajst-25954	232	3	sharing	sharing	NOUN
ajst-25954	232	4	increases	increase	NOUN
ajst-25954	232	5	,	,	PUNCT
ajst-25954	232	6	cross	cross	ADJ
ajst-25954	232	7	-	-	ADJ
ajst-25954	232	8	institutional	institutional	ADJ
ajst-25954	232	9	,	,	PUNCT
ajst-25954	232	10	cross	cross	ADJ
ajst-25954	232	11	-	-	ADJ
ajst-25954	232	12	international	international	ADJ
ajst-25954	232	13	collaborative	collaborative	ADJ
ajst-25954	232	14	research	research	NOUN
ajst-25954	232	15	promotes	promote	VERB
ajst-25954	232	16	more	more	ADV
ajst-25954	232	17	comprehensive	comprehensive	ADJ
ajst-25954	232	18	seizure	seizure	NOUN
ajst-25954	232	19	prediction	prediction	NOUN
ajst-25954	232	20	models	model	NOUN
ajst-25954	232	21	.	.	PUNCT
ajst-25954	233	1	these	these	DET
ajst-25954	233	2	efforts	effort	NOUN
ajst-25954	233	3	are	be	AUX
ajst-25954	233	4	expected	expect	VERB
ajst-25954	233	5	to	to	PART
ajst-25954	233	6	provide	provide	VERB
ajst-25954	233	7	more	more	ADV
ajst-25954	233	8	effective	effective	ADJ
ajst-25954	233	9	treatment	treatment	NOUN
ajst-25954	233	10	and	and	CCONJ
ajst-25954	233	11	improved	improve	VERB
ajst-25954	233	12	quality	quality	NOUN
ajst-25954	233	13	of	of	ADP
ajst-25954	233	14	life	life	NOUN
ajst-25954	233	15	for	for	ADP
ajst-25954	233	16	people	people	NOUN
ajst-25954	233	17	with	with	ADP
ajst-25954	233	18	epilepsy	epilepsy	NOUN
ajst-25954	233	19	.	.	PUNCT
ajst-25954	234	1	references	reference	NOUN
ajst-25954	234	2	[	[	X
ajst-25954	234	3	1	1	NUM
ajst-25954	234	4	]	]	PUNCT
ajst-25954	234	5	world	world	NOUN
ajst-25954	234	6	health	health	NOUN
ajst-25954	234	7	organization	organization	NOUN
ajst-25954	234	8	.	.	PUNCT
ajst-25954	235	1	[	[	X
ajst-25954	235	2	online	online	X
ajst-25954	235	3	]	]	X
ajst-25954	235	4	.	.	PUNCT
ajst-25954	236	1	available	available	ADJ
ajst-25954	236	2	:	:	PUNCT
ajst-25954	236	3	https://www.who.int/ne	https://www.who.int/ne	PROPN
ajst-25954	236	4	ws	ws	PROPN
ajst-25954	236	5	/	/	SYM
ajst-25954	236	6	item/27	item/27	PROPN
ajst-25954	236	7	-	-	PUNCT
ajst-25954	236	8	05	05	NUM
ajst-25954	236	9	-	-	PUNCT
ajst-25954	236	10	2022	2022	NUM
ajst-25954	236	11	-	-	PUNCT
ajst-25954	236	12	seventy	seventy	NUM
ajst-25954	236	13	-	-	PUNCT
ajst-25954	236	14	fifthworld	fifthworld	NOUN
ajst-25954	236	15	-	-	PUNCT
ajst-25954	236	16	health	health	NOUN
ajst-25954	236	17	-	-	PUNCT
ajst-25954	236	18	asse	asse	NOUN
ajst-25954	236	19	mbly	mbly	NOUN
ajst-25954	236	20	---	---	PUNCT
ajst-25954	236	21	daily	daily	ADJ
ajst-25954	236	22	-	-	PUNCT
ajst-25954	236	23	upda	upda	ADJ
ajst-25954	236	24	te--27	te--27	NOUN
ajst-25954	236	25	-	-	PUNCT
ajst-25954	236	26	may-2022	may-2022	NOUN
ajst-25954	236	27	,	,	PUNCT
ajst-25954	236	28	accessed	access	VERB
ajst-25954	236	29	on	on	ADP
ajst-25954	236	30	:	:	PUNCT
ajst-25954	236	31	october	october	PROPN
ajst-25954	236	32	.	.	PROPN
ajst-25954	237	1	6	6	NUM
ajst-25954	237	2	,	,	PUNCT
ajst-25954	237	3	2022	2022	NUM
ajst-25954	237	4	.	.	PUNCT
ajst-25954	238	1	[	[	X
ajst-25954	238	2	2	2	NUM
ajst-25954	238	3	]	]	PUNCT
ajst-25954	238	4	a.	a.	NOUN
ajst-25954	238	5	shoeibi	shoeibi	NOUN
ajst-25954	238	6	et	et	PROPN
ajst-25954	238	7	al	al	PROPN
ajst-25954	238	8	.	.	PROPN
ajst-25954	238	9	,	,	PUNCT
ajst-25954	238	10	“	"	PUNCT
ajst-25954	238	11	a	a	DET
ajst-25954	238	12	comprehensive	comprehensive	ADJ
ajst-25954	238	13	comparison	comparison	NOUN
ajst-25954	238	14	of	of	ADP
ajst-25954	238	15	handcrafted	handcraft	VERB
ajst-25954	238	16	features	feature	NOUN
ajst-25954	238	17	and	and	CCONJ
ajst-25954	238	18	convolutional	convolutional	ADJ
ajst-25954	238	19	autoencoders	autoencoder	NOUN
ajst-25954	238	20	for	for	ADP
ajst-25954	238	21	epileptic	epileptic	ADJ
ajst-25954	238	22	seizures	seizure	NOUN
ajst-25954	238	23	detection	detection	NOUN
ajst-25954	238	24	in	in	ADP
ajst-25954	238	25	eegs	eegs	PROPN
ajst-25954	238	26	ignals	ignal	NOUN
ajst-25954	238	27	,	,	PUNCT
ajst-25954	238	28	”	"	PUNCT
ajst-25954	238	29	expert	expert	NOUN
ajst-25954	238	30	syst	syst	NOUN
ajst-25954	238	31	.	.	PUNCT
ajst-25954	239	1	appl	appl	PROPN
ajst-25954	239	2	.	.	PROPN
ajst-25954	239	3	,	,	PUNCT
ajst-25954	239	4	vol	vol	NOUN
ajst-25954	239	5	.	.	PROPN
ajst-25954	239	6	163	163	NUM
ajst-25954	239	7	,	,	PUNCT
ajst-25954	239	8	2021	2021	NUM
ajst-25954	239	9	,	,	PUNCT
ajst-25954	239	10	art	art	NOUN
ajst-25954	239	11	.	.	PUNCT
ajst-25954	240	1	no	no	INTJ
ajst-25954	240	2	.	.	NOUN
ajst-25954	241	1	113788	113788	NUM
ajst-25954	241	2	.	.	PUNCT
ajst-25954	242	1	[	[	X
ajst-25954	242	2	3	3	X
ajst-25954	242	3	]	]	X
ajst-25954	242	4	s.	s.	PROPN
ajst-25954	242	5	chakrabarti	chakrabarti	PROPN
ajst-25954	242	6	,	,	PUNCT
ajst-25954	242	7	a.	a.	NOUN
ajst-25954	242	8	swetapadma	swetapadma	NOUN
ajst-25954	242	9	,	,	PUNCT
ajst-25954	242	10	and	and	CCONJ
ajst-25954	242	11	p.	p.	PROPN
ajst-25954	242	12	k.	k.	PROPN
ajst-25954	242	13	pattnaik	pattnaik	PROPN
ajst-25954	242	14	,	,	PUNCT
ajst-25954	242	15	“	"	PUNCT
ajst-25954	242	16	a	a	DET
ajst-25954	242	17	channel	channel	NOUN
ajst-25954	242	18	in	in	ADP
ajst-25954	242	19	dependent	dependent	ADJ
ajst-25954	242	20	generalized	generalize	VERB
ajst-25954	242	21	seizure	seizure	NOUN
ajst-25954	242	22	detection	detection	NOUN
ajst-25954	242	23	method	method	NOUN
ajst-25954	242	24	for	for	ADP
ajst-25954	242	25	pediatric	pediatric	ADJ
ajst-25954	242	26	epileptic	epileptic	ADJ
ajst-25954	242	27	seizures,”comput	seizures,”comput	NOUN
ajst-25954	242	28	.	.	PUNCT
ajst-25954	243	1	methods	method	NOUN
ajst-25954	243	2	programs	program	NOUN
ajst-25954	243	3	biomed	biome	VERB
ajst-25954	243	4	.	.	PUNCT
ajst-25954	244	1	,	,	PUNCT
ajst-25954	244	2	vol	vol	NOUN
ajst-25954	244	3	.	.	PUNCT
ajst-25954	245	1	209	209	NUM
ajst-25954	245	2	,	,	PUNCT
ajst-25954	245	3	2021	2021	NUM
ajst-25954	245	4	,	,	PUNCT
ajst-25954	245	5	art	art	NOUN
ajst-25954	245	6	.	.	PUNCT
ajst-25954	246	1	no.106335	no.106335	ADJ
ajst-25954	246	2	.	.	PUNCT
ajst-25954	247	1	[	[	X
ajst-25954	247	2	4	4	X
ajst-25954	247	3	]	]	PUNCT
ajst-25954	247	4	h.	h.	PROPN
ajst-25954	247	5	daoud	daoud	PROPN
ajst-25954	247	6	and	and	CCONJ
ajst-25954	247	7	m.	m.	NOUN
ajst-25954	247	8	bayoumi	bayoumi	NOUN
ajst-25954	247	9	,	,	PUNCT
ajst-25954	247	10	“	"	PUNCT
ajst-25954	247	11	deep	deep	ADJ
ajst-25954	247	12	learning	learning	NOUN
ajst-25954	247	13	approach	approach	NOUN
ajst-25954	247	14	for	for	ADP
ajst-25954	247	15	epileptic	epileptic	ADJ
ajst-25954	247	16	focus	focus	NOUN
ajst-25954	247	17	localization,”ieee	localization,”ieee	NOUN
ajst-25954	247	18	trans.biomed	trans.biome	VERB
ajst-25954	247	19	.	.	PUNCT
ajst-25954	247	20	circuits	circuit	NOUN
ajst-25954	247	21	syst	syst	PROPN
ajst-25954	247	22	.	.	PUNCT
ajst-25954	247	23	,	,	PUNCT
ajst-25954	247	24	vol	vol	NOUN
ajst-25954	247	25	.	.	PROPN
ajst-25954	248	1	14	14	NUM
ajst-25954	248	2	,	,	PUNCT
ajst-25954	248	3	no	no	INTJ
ajst-25954	248	4	.	.	NOUN
ajst-25954	248	5	2	2	NUM
ajst-25954	248	6	,	,	PUNCT
ajst-25954	248	7	pp	pp	ADJ
ajst-25954	248	8	.	.	PUNCT
ajst-25954	249	1	209–220,apr	209–220,apr	X
ajst-25954	249	2	.	.	PUNCT
ajst-25954	250	1	2020	2020	NUM
ajst-25954	250	2	.	.	PUNCT
ajst-25954	251	1	[	[	X
ajst-25954	251	2	5	5	NUM
ajst-25954	251	3	]	]	X
ajst-25954	251	4	c.	c.	PROPN
ajst-25954	251	5	a.teixeira	a.teixeira	PROPN
ajst-25954	251	6	et	et	PROPN
ajst-25954	251	7	al	al	PROPN
ajst-25954	251	8	.	.	PROPN
ajst-25954	251	9	,	,	PUNCT
ajst-25954	251	10	“	"	PUNCT
ajst-25954	251	11	epileptic	epileptic	ADJ
ajst-25954	251	12	seizure	seizure	NOUN
ajst-25954	251	13	predictors	predictor	NOUN
ajst-25954	251	14	based	base	VERB
ajst-25954	251	15	on	on	ADP
ajst-25954	251	16	computational	computational	ADJ
ajst-25954	251	17	intelligence	intelligence	NOUN
ajst-25954	251	18	techniques	technique	NOUN
ajst-25954	251	19	:	:	PUNCT
ajst-25954	251	20	a	a	DET
ajst-25954	251	21	comparative	comparative	ADJ
ajst-25954	251	22	study	study	NOUN
ajst-25954	251	23	with	with	ADP
ajst-25954	251	24	278	278	NUM
ajst-25954	251	25	patients	patient	NOUN
ajst-25954	251	26	,	,	PUNCT
ajst-25954	251	27	”	"	PUNCT
ajst-25954	251	28	comput	comput	NOUN
ajst-25954	251	29	.	.	PUNCT
ajst-25954	252	1	methods	method	NOUN
ajst-25954	252	2	programs	program	NOUN
ajst-25954	252	3	biomed	biome	VERB
ajst-25954	252	4	.	.	PUNCT
ajst-25954	253	1	,	,	PUNCT
ajst-25954	253	2	vol	vol	NOUN
ajst-25954	253	3	.	.	PROPN
ajst-25954	253	4	114	114	NUM
ajst-25954	253	5	,	,	PUNCT
ajst-25954	253	6	no	no	INTJ
ajst-25954	253	7	.	.	NOUN
ajst-25954	253	8	3	3	NUM
ajst-25954	253	9	,	,	PUNCT
ajst-25954	253	10	pp	pp	ADJ
ajst-25954	253	11	.	.	PUNCT
ajst-25954	254	1	324–336	324–336	NUM
ajst-25954	254	2	,	,	PUNCT
ajst-25954	254	3	2014	2014	NUM
ajst-25954	254	4	.	.	PUNCT
ajst-25954	255	1	[	[	X
ajst-25954	255	2	6	6	NUM
ajst-25954	255	3	]	]	X
ajst-25954	255	4	wang	wang	PROPN
ajst-25954	255	5	,	,	PUNCT
ajst-25954	255	6	y.	y.	PROPN
ajst-25954	255	7	,	,	PUNCT
ajst-25954	255	8	et	et	PROPN
ajst-25954	255	9	al	al	PROPN
ajst-25954	255	10	.	.	PROPN
ajst-25954	255	11	,	,	PUNCT
ajst-25954	255	12	a	a	DET
ajst-25954	255	13	spatiotemporal	spatiotemporal	ADJ
ajst-25954	255	14	graph	graph	NOUN
ajst-25954	255	15	attention	attention	NOUN
ajst-25954	255	16	network	network	NOUN
ajst-25954	255	17	based	base	VERB
ajst-25954	255	18	on	on	ADP
ajst-25954	255	19	s	s	PROPN
ajst-25954	255	20	ynchronization	ynchronization	NOUN
ajst-25954	255	21	for	for	ADP
ajst-25954	255	22	epileptic	epileptic	ADJ
ajst-25954	255	23	seizure	seizure	NOUN
ajst-25954	255	24	prediction	prediction	NOUN
ajst-25954	255	25	.	.	PUNCT
ajst-25954	256	1	ieee	ieee	PROPN
ajst-25954	256	2	journal	journal	PROPN
ajst-25954	256	3	of	of	ADP
ajst-25954	256	4	bi	bi	PROPN
ajst-25954	256	5	omedical	omedical	ADJ
ajst-25954	256	6	and	and	CCONJ
ajst-25954	256	7	health	health	NOUN
ajst-25954	256	8	informatics	informatic	NOUN
ajst-25954	256	9	,	,	PUNCT
ajst-25954	256	10	2023	2023	NUM
ajst-25954	256	11	.	.	PUNCT
ajst-25954	257	1	27(2	27(2	NUM
ajst-25954	257	2	):	):	PUNCT
ajst-25954	257	3	p.	p.	NOUN
ajst-25954	257	4	900	900	NUM
ajst-25954	257	5	-	-	SYM
ajst-25954	257	6	911	911	NUM
ajst-25954	257	7	.	.	PUNCT
ajst-25954	258	1	[	[	X
ajst-25954	258	2	7	7	X
ajst-25954	258	3	]	]	PUNCT
ajst-25954	258	4	k.	k.	PROPN
ajst-25954	258	5	m.	m.	PROPN
ajst-25954	258	6	tsiouris	tsiouris	PROPN
ajst-25954	258	7	,	,	PUNCT
ajst-25954	258	8	v.	v.	PROPN
ajst-25954	258	9	c.	c.	PROPN
ajst-25954	258	10	pezoulas	pezoulas	PROPN
ajst-25954	258	11	,	,	PUNCT
ajst-25954	258	12	m.	m.	NOUN
ajst-25954	258	13	zervakis	zervakis	PROPN
ajst-25954	258	14	,	,	PUNCT
ajst-25954	258	15	s.	s.	PROPN
ajst-25954	258	16	konitsiotis	konitsiotis	PROPN
ajst-25954	258	17	,	,	PUNCT
ajst-25954	258	18	d.	d.	PROPN
ajst-25954	258	19	d.	d.	PROPN
ajst-25954	258	20	koutsouris	koutsouris	PROPN
ajst-25954	258	21	,	,	PUNCT
ajst-25954	258	22	and	and	CCONJ
ajst-25954	258	23	d.	d.	PROPN
ajst-25954	258	24	i.	i.	PROPN
ajst-25954	258	25	fotiadis	fotiadis	PROPN
ajst-25954	258	26	,	,	PUNCT
ajst-25954	258	27	“	"	PUNCT
ajst-25954	258	28	a	a	DET
ajst-25954	258	29	long	long	ADJ
ajst-25954	258	30	short	short	ADJ
ajst-25954	258	31	-	-	PUNCT
ajst-25954	258	32	term	term	NOUN
ajst-25954	258	33	memory	memory	NOUN
ajst-25954	258	34	deep	deep	ADJ
ajst-25954	258	35	learning	learning	NOUN
ajst-25954	258	36	network	network	NOUN
ajst-25954	258	37	for	for	ADP
ajst-25954	258	38	the	the	DET
ajst-25954	258	39	prediction	prediction	NOUN
ajst-25954	258	40	of	of	ADP
ajst-25954	258	41	epileptic	epileptic	ADJ
ajst-25954	258	42	seizures	seizure	NOUN
ajst-25954	258	43	using	use	VERB
ajst-25954	258	44	eeg	eeg	NOUN
ajst-25954	258	45	signals	signal	NOUN
ajst-25954	258	46	,	,	PUNCT
ajst-25954	258	47	”	"	PUNCT
ajst-25954	258	48	comput.biol	comput.biol	PROPN
ajst-25954	258	49	.	.	PUNCT
ajst-25954	259	1	med	med	PROPN
ajst-25954	259	2	.	.	PROPN
ajst-25954	259	3	,	,	PUNCT
ajst-25954	259	4	vol	vol	NOUN
ajst-25954	259	5	.	.	PROPN
ajst-25954	260	1	99	99	NUM
ajst-25954	260	2	,	,	PUNCT
ajst-25954	260	3	pp	pp	ADJ
ajst-25954	260	4	.	.	PUNCT
ajst-25954	261	1	24–37	24–37	NUM
ajst-25954	261	2	,	,	PUNCT
ajst-25954	261	3	2018	2018	NUM
ajst-25954	261	4	.	.	PUNCT
ajst-25954	262	1	[	[	X
ajst-25954	262	2	8	8	NUM
ajst-25954	262	3	]	]	PUNCT
ajst-25954	262	4	x.	x.	NOUN
ajst-25954	262	5	wei	wei	PROPN
ajst-25954	262	6	,	,	PUNCT
ajst-25954	262	7	l.	l.	PROPN
ajst-25954	262	8	zhou	zhou	PROPN
ajst-25954	262	9	,	,	PUNCT
ajst-25954	262	10	z.	z.	PROPN
ajst-25954	262	11	zhang	zhang	PROPN
ajst-25954	262	12	,	,	PUNCT
ajst-25954	262	13	z.	z.	PROPN
ajst-25954	262	14	chen	chen	PROPN
ajst-25954	262	15	,	,	PUNCT
ajst-25954	262	16	and	and	CCONJ
ajst-25954	262	17	y.	y.	PROPN
ajst-25954	262	18	zhou	zhou	PROPN
ajst-25954	262	19	,	,	PUNCT
ajst-25954	262	20	“	"	PUNCT
ajst-25954	262	21	early	early	ADJ
ajst-25954	262	22	prediction	prediction	NOUN
ajst-25954	262	23	of	of	ADP
ajst-25954	262	24	epileptic	epileptic	ADJ
ajst-25954	262	25	seizures	seizure	NOUN
ajst-25954	262	26	using	use	VERB
ajst-25954	262	27	a	a	DET
ajst-25954	262	28	long	long	ADJ
ajst-25954	262	29	-	-	PUNCT
ajst-25954	262	30	term	term	NOUN
ajst-25954	262	31	recurrent	recurrent	ADJ
ajst-25954	262	32	convolutional	convolutional	ADJ
ajst-25954	262	33	network	network	NOUN
ajst-25954	262	34	,	,	PUNCT
ajst-25954	262	35	”	"	PUNCT
ajst-25954	262	36	j.	j.	PROPN
ajst-25954	262	37	neurosci	neurosci	PROPN
ajst-25954	262	38	.	.	PUNCT
ajst-25954	263	1	methods	method	NOUN
ajst-25954	263	2	,	,	PUNCT
ajst-25954	263	3	vol	vol	NOUN
ajst-25954	263	4	.	.	PROPN
ajst-25954	263	5	327	327	NUM
ajst-25954	263	6	,	,	PUNCT
ajst-25954	263	7	2019	2019	NUM
ajst-25954	263	8	,	,	PUNCT
ajst-25954	263	9	art	art	NOUN
ajst-25954	263	10	.	.	PUNCT
ajst-25954	264	1	no	no	INTJ
ajst-25954	264	2	.	.	NOUN
ajst-25954	265	1	108395	108395	NUM
ajst-25954	265	2	.	.	PUNCT
ajst-25954	266	1	[	[	X
ajst-25954	266	2	9	9	NUM
ajst-25954	266	3	]	]	X
ajst-25954	266	4	n.	n.	PROPN
ajst-25954	266	5	d.	d.	PROPN
ajst-25954	266	6	truong	truong	PROPN
ajst-25954	266	7	et	et	PROPN
ajst-25954	266	8	al	al	PROPN
ajst-25954	266	9	.	.	PROPN
ajst-25954	266	10	,	,	PUNCT
ajst-25954	266	11	“	"	PUNCT
ajst-25954	266	12	convolutional	convolutional	ADJ
ajst-25954	266	13	neural	neural	ADJ
ajst-25954	266	14	networks	network	NOUN
ajst-25954	266	15	for	for	ADP
ajst-25954	266	16	seizure	seizure	NOUN
ajst-25954	266	17	prediction	prediction	NOUN
ajst-25954	266	18	using	use	VERB
ajst-25954	266	19	intracranial	intracranial	ADJ
ajst-25954	266	20	and	and	CCONJ
ajst-25954	266	21	scalp	scalp	NOUN
ajst-25954	266	22	electroencephalogram	electroencephalogram	NOUN
ajst-25954	266	23	,	,	PUNCT
ajst-25954	266	24	”	"	PUNCT
ajst-25954	266	25	neural	neural	ADJ
ajst-25954	266	26	netwvol	netwvol	NOUN
ajst-25954	266	27	.	.	PUNCT
ajst-25954	267	1	105	105	NUM
ajst-25954	267	2	,	,	PUNCT
ajst-25954	267	3	pp	pp	ADJ
ajst-25954	267	4	.	.	PUNCT
ajst-25954	268	1	104–111	104–111	NUM
ajst-25954	268	2	,	,	PUNCT
ajst-25954	268	3	2018	2018	NUM
ajst-25954	268	4	.	.	PUNCT
ajst-25954	269	1	[	[	X
ajst-25954	269	2	10	10	NUM
ajst-25954	269	3	]	]	X
ajst-25954	269	4	lu	lu	PROPN
ajst-25954	269	5	,	,	PUNCT
ajst-25954	269	6	x.	x.	PROPN
ajst-25954	269	7	,	,	PUNCT
ajst-25954	269	8	zhang	zhang	PROPN
ajst-25954	269	9	,	,	PUNCT
ajst-25954	269	10	j.	j.	PROPN
ajst-25954	269	11	,	,	PUNCT
ajst-25954	269	12	huang	huang	PROPN
ajst-25954	269	13	,	,	PUNCT
ajst-25954	269	14	s.	s.	PROPN
ajst-25954	269	15	,	,	PUNCT
ajst-25954	269	16	lu	lu	PROPN
ajst-25954	269	17	,	,	PUNCT
ajst-25954	269	18	j.	j.	PROPN
ajst-25954	269	19	,	,	PUNCT
ajst-25954	269	20	ye	ye	PROPN
ajst-25954	269	21	,	,	PUNCT
ajst-25954	269	22	m.	m.	NOUN
ajst-25954	269	23	,	,	PUNCT
ajst-25954	269	24	and	and	CCONJ
ajst-25954	269	25	wang	wang	PROPN
ajst-25954	269	26	,	,	PUNCT
ajst-25954	269	27	m.	m.	NOUN
ajst-25954	269	28	,	,	PUNCT
ajst-25954	269	29	“	"	PUNCT
ajst-25954	269	30	detection	detection	NOUN
ajst-25954	269	31	and	and	CCONJ
ajst-25954	269	32	classification	classification	NOUN
ajst-25954	269	33	of	of	ADP
ajst-25954	269	34	epileptic	epileptic	ADJ
ajst-25954	269	35	eeg	eeg	NOUN
ajst-25954	269	36	signals	signal	NOUN
ajst-25954	269	37	by	by	ADP
ajst-25954	269	38	the	the	DET
ajst-25954	269	39	methods	method	NOUN
ajst-25954	269	40	of	of	ADP
ajst-25954	269	41	nonlinear	nonlinear	ADJ
ajst-25954	269	42	dynamics	dynamic	NOUN
ajst-25954	269	43	”	"	PUNCT
ajst-25954	269	44	.	.	PUNCT
ajst-25954	270	1	chaossolitons	chaossoliton	NOUN
ajst-25954	270	2	fractals	fractal	VERB
ajst-25954	270	3	151:111032	151:111032	NUM
ajst-25954	270	4	.	.	PUNCT
ajst-25954	270	5	doi:10.1016	doi:10.1016	PROPN
ajst-25954	270	6	/	/	SYM
ajst-25954	270	7	j.chaos.2021.111032	j.chaos.2021.111032	PROPN
ajst-25954	270	8	.	.	PUNCT
ajst-25954	271	1	[	[	X
ajst-25954	271	2	11	11	NUM
ajst-25954	271	3	]	]	SYM
ajst-25954	271	4	yu	yu	PROPN
ajst-25954	271	5	,	,	PUNCT
ajst-25954	271	6	z.y	z.y	PROPN
ajst-25954	271	7	.	.	PROPN
ajst-25954	271	8	,	,	PUNCT
ajst-25954	271	9	et	et	PROPN
ajst-25954	271	10	al	al	PROPN
ajst-25954	271	11	.	.	PROPN
ajst-25954	271	12	,	,	PUNCT
ajst-25954	271	13	epileptic	epileptic	ADJ
ajst-25954	271	14	seizure	seizure	NOUN
ajst-25954	271	15	prediction	prediction	NOUN
ajst-25954	271	16	using	use	VERB
ajst-25954	271	17	deep	deep	ADJ
ajst-25954	271	18	neural	neural	ADJ
ajst-25954	271	19	network	network	NOUN
ajst-25954	271	20	via	via	ADP
ajst-25954	271	21	transfer	transfer	NOUN
ajst-25954	271	22	learning	learning	NOUN
ajst-25954	271	23	and	and	CCONJ
ajst-25954	271	24	multi	multi	ADJ
ajst-25954	271	25	-	-	ADJ
ajst-25954	271	26	feature	feature	ADJ
ajst-25954	271	27	fusion	fusion	NOUN
ajst-25954	271	28	.	.	PUNCT
ajst-25954	272	1	international	international	ADJ
ajst-25954	272	2	journal	journal	PROPN
ajst-25954	272	3	of	of	ADP
ajst-25954	272	4	neural	neural	ADJ
ajst-25954	272	5	systems	system	NOUN
ajst-25954	272	6	,	,	PUNCT
ajst-25954	272	7	2022	2022	NUM
ajst-25954	272	8	.	.	PUNCT
ajst-25954	273	1	32(07	32(07	NUM
ajst-25954	273	2	)	)	PUNCT
ajst-25954	273	3	.	.	PUNCT
ajst-25954	274	1	91	91	NUM
ajst-25954	275	1	[	[	X
ajst-25954	275	2	12	12	NUM
ajst-25954	275	3	]	]	X
ajst-25954	275	4	c.	c.	PROPN
ajst-25954	275	5	chen	chen	PROPN
ajst-25954	275	6	,	,	PUNCT
ajst-25954	275	7	j.	j.	PROPN
ajst-25954	275	8	liu	liu	PROPN
ajst-25954	275	9	,	,	PUNCT
ajst-25954	275	10	and	and	CCONJ
ajst-25954	275	11	j.	j.	PROPN
ajst-25954	275	12	syu	syu	PROPN
ajst-25954	275	13	,	,	PUNCT
ajst-25954	275	14	‘	'	PUNCT
ajst-25954	275	15	‘	'	PUNCT
ajst-25954	275	16	application	application	NOUN
ajst-25954	275	17	of	of	ADP
ajst-25954	275	18	chaos	chaos	NOUN
ajst-25954	275	19	theory	theory	NOUN
ajst-25954	275	20	and	and	CCONJ
ajst-25954	275	21	data	datum	NOUN
ajst-25954	275	22	mining	mining	NOUN
ajst-25954	275	23	to	to	PART
ajst-25954	275	24	seizure	seizure	VERB
ajst-25954	275	25	detection	detection	NOUN
ajst-25954	275	26	of	of	ADP
ajst-25954	275	27	epilepsy	epilepsy	NOUN
ajst-25954	275	28	,	,	PUNCT
ajst-25954	275	29	’’	’'	PUNCT
ajst-25954	275	30	in	in	ADP
ajst-25954	275	31	proc	proc	NOUN
ajst-25954	275	32	.	.	PUNCT
ajst-25954	276	1	conf	conf	NOUN
ajst-25954	276	2	.	.	PUNCT
ajst-25954	277	1	ipcsit	ipcsit	PROPN
ajst-25954	277	2	/	/	SYM
ajst-25954	277	3	hong	hong	PROPN
ajst-25954	277	4	kong	kong	PROPN
ajst-25954	277	5	,	,	PUNCT
ajst-25954	277	6	vol	vol	NOUN
ajst-25954	277	7	.	.	PUNCT
ajst-25954	278	1	25,2012	25,2012	NUM
ajst-25954	278	2	,	,	PUNCT
ajst-25954	278	3	pp	pp	ADV
ajst-25954	278	4	.	.	PUNCT
ajst-25954	279	1	23–28	23–28	NUM
ajst-25954	279	2	.	.	PUNCT
ajst-25954	280	1	[	[	X
ajst-25954	280	2	13	13	NUM
ajst-25954	280	3	]	]	PUNCT
ajst-25954	280	4	k.	k.	PROPN
ajst-25954	280	5	t.	t.	PROPN
ajst-25954	280	6	tapani	tapani	PROPN
ajst-25954	280	7	,	,	PUNCT
ajst-25954	280	8	s.	s.	PROPN
ajst-25954	280	9	vanhatalo	vanhatalo	PROPN
ajst-25954	280	10	,	,	PUNCT
ajst-25954	280	11	and	and	CCONJ
ajst-25954	280	12	n.	n.	PROPN
ajst-25954	280	13	j.	j.	PROPN
ajst-25954	280	14	stevenson	stevenson	PROPN
ajst-25954	280	15	,	,	PUNCT
ajst-25954	280	16	‘	'	PUNCT
ajst-25954	280	17	‘	'	PUNCT
ajst-25954	280	18	time	time	NOUN
ajst-25954	280	19	varying	vary	VERB
ajst-25954	280	20	eeg	eeg	NOUN
ajst-25954	280	21	correlations	correlation	NOUN
ajst-25954	280	22	improve	improve	VERB
ajst-25954	280	23	automated	automate	VERB
ajst-25954	280	24	neonatal	neonatal	ADJ
ajst-25954	280	25	seizure	seizure	NOUN
ajst-25954	280	26	detection	detection	NOUN
ajst-25954	280	27	,	,	PUNCT
ajst-25954	280	28	’’	’'	PUNCT
ajst-25954	280	29	int	int	NOUN
ajst-25954	280	30	.	.	PUNCT
ajst-25954	281	1	j.	j.	PROPN
ajst-25954	281	2	neural	neural	PROPN
ajst-25954	281	3	syst	syst	PROPN
ajst-25954	281	4	.	.	PUNCT
ajst-25954	281	5	,	,	PUNCT
ajst-25954	281	6	vol	vol	NOUN
ajst-25954	281	7	.	.	PROPN
ajst-25954	281	8	29	29	NUM
ajst-25954	281	9	,	,	PUNCT
ajst-25954	281	10	no	no	INTJ
ajst-25954	281	11	.	.	NOUN
ajst-25954	281	12	4	4	NUM
ajst-25954	281	13	,	,	PUNCT
ajst-25954	281	14	may	may	AUX
ajst-25954	281	15	2019	2019	NUM
ajst-25954	281	16	,	,	PUNCT
ajst-25954	281	17	art	art	NOUN
ajst-25954	281	18	.	.	PUNCT
ajst-25954	282	1	no	no	INTJ
ajst-25954	282	2	.	.	NOUN
ajst-25954	283	1	1850030	1850030	NUM
ajst-25954	283	2	.	.	PUNCT
ajst-25954	284	1	[	[	X
ajst-25954	284	2	14	14	NUM
ajst-25954	284	3	]	]	PUNCT
ajst-25954	284	4	he	he	PRON
ajst-25954	284	5	,	,	PUNCT
ajst-25954	284	6	j.	j.	PROPN
ajst-25954	284	7	,	,	PUNCT
ajst-25954	284	8	cui	cui	PROPN
ajst-25954	284	9	,	,	PUNCT
ajst-25954	284	10	j.	j.	PROPN
ajst-25954	284	11	,	,	PUNCT
ajst-25954	284	12	zhang	zhang	PROPN
ajst-25954	284	13	,	,	PUNCT
ajst-25954	284	14	g.	g.	PROPN
ajst-25954	284	15	,	,	PUNCT
ajst-25954	284	16	xue	xue	PROPN
ajst-25954	284	17	,	,	PUNCT
ajst-25954	284	18	m.	m.	NOUN
ajst-25954	284	19	,	,	PUNCT
ajst-25954	284	20	chu	chu	PROPN
ajst-25954	284	21	,	,	PUNCT
ajst-25954	284	22	d.	d.	PROPN
ajst-25954	284	23	,	,	PUNCT
ajst-25954	284	24	and	and	CCONJ
ajst-25954	284	25	zhao	zhao	PROPN
ajst-25954	284	26	,	,	PUNCT
ajst-25954	284	27	y.	y.	PROPN
ajst-25954	284	28	,	,	PUNCT
ajst-25954	284	29	“	"	PUNCT
ajst-25954	284	30	spatial	spatial	ADJ
ajst-25954	284	31	-	-	PUNCT
ajst-25954	284	32	temoral	temoral	ADJ
ajst-25954	284	33	seizure	seizure	NOUN
ajst-25954	284	34	detection	detection	NOUN
ajst-25954	284	35	with	with	ADP
ajst-25954	284	36	graph	graph	NOUN
ajst-25954	284	37	att	att	PROPN
ajst-25954	284	38	-	-	PUNCT
ajst-25954	284	39	ention	ention	NOUN
ajst-25954	284	40	network	network	NOUN
ajst-25954	284	41	and	and	CCONJ
ajst-25954	284	42	bi	bi	ADJ
ajst-25954	284	43	-	-	ADJ
ajst-25954	284	44	directional	directional	ADJ
ajst-25954	284	45	lstm	lstm	NOUN
ajst-25954	284	46	architecture	architecture	NOUN
ajst-25954	284	47	”	"	PUNCT
ajst-25954	284	48	.	.	PUNCT
ajst-25954	285	1	bi	bi	NOUN
ajst-25954	285	2	-	-	ADJ
ajst-25954	285	3	omed	ome	VERB
ajst-25954	285	4	.	.	PUNCT
ajst-25954	286	1	signal	signal	PROPN
ajst-25954	286	2	proc	proc	PROPN
ajst-25954	286	3	.	.	PUNCT
ajst-25954	287	1	control	control	NOUN
ajst-25954	287	2	78:103908.doi	78:103908.doi	NUM
ajst-25954	287	3	:	:	PUNCT
ajst-25954	287	4	10.1016	10.1016	NUM
ajst-25954	287	5	/j.bspc.2022.103908	/j.bspc.2022.103908	PUNCT
ajst-25954	287	6	.	.	PUNCT
ajst-25954	288	1	[	[	X
ajst-25954	288	2	15	15	NUM
ajst-25954	288	3	]	]	X
ajst-25954	288	4	ra	ra	PROPN
ajst-25954	288	5	,	,	PUNCT
ajst-25954	288	6	j.s	j.s	PROPN
ajst-25954	288	7	.	.	PROPN
ajst-25954	288	8	,t	,t	PROPN
ajst-25954	288	9	.	.	PUNCT
ajst-25954	289	1	li	li	PROPN
ajst-25954	289	2	and	and	CCONJ
ajst-25954	289	3	yanli	yanli	PROPN
ajst-25954	289	4	,	,	PUNCT
ajst-25954	289	5	a	a	DET
ajst-25954	289	6	novel	novel	ADJ
ajst-25954	289	7	epileptic	epileptic	NOUN
ajst-25954	289	8	seizure	seizure	NOUN
ajst-25954	289	9	prediction	prediction	NOUN
ajst-25954	289	10	method	method	NOUN
ajst-25954	289	11	based	base	VERB
ajst-25954	289	12	on	on	ADP
ajst-25954	289	13	synchro	synchro	NOUN
ajst-25954	289	14	extracting	extracting	NOUN
ajst-25954	289	15	transform	transform	NOUN
ajst-25954	289	16	and	and	CCONJ
ajst-25954	289	17	1dimensional	1dimensional	NUM
ajst-25954	289	18	convolutional	convolutional	ADJ
ajst-25954	289	19	neural	neural	ADJ
ajst-25954	289	20	network	network	NOUN
ajst-25954	289	21	.	.	PUNCT
ajst-25954	290	1	compu	compu	PROPN
ajst-25954	290	2	-	-	PUNCT
ajst-25954	290	3	ter	ter	NOUN
ajst-25954	290	4	methods	method	NOUN
ajst-25954	290	5	and	and	CCONJ
ajst-25954	290	6	programs	program	NOUN
ajst-25954	290	7	in	in	ADP
ajst-25954	290	8	biomedicine,2023.240	biomedicine,2023.240	NOUN
ajst-25954	290	9	.	.	PUNCT
ajst-25954	291	1	[	[	X
ajst-25954	291	2	16	16	NUM
ajst-25954	291	3	]	]	X
ajst-25954	291	4	sun	sun	PROPN
ajst-25954	291	5	,	,	PUNCT
ajst-25954	291	6	b.	b.	PROPN
ajst-25954	291	7	et	et	PROPN
ajst-25954	291	8	al	al	PROPN
ajst-25954	291	9	.	.	PROPN
ajst-25954	292	1	seizure	seizure	NOUN
ajst-25954	292	2	prediction	prediction	NOUN
ajst-25954	292	3	in	in	ADP
ajst-25954	292	4	scalp	scalp	NOUN
ajst-25954	292	5	eeg	eeg	NOUN
ajst-25954	292	6	based	base	VERB
ajst-25954	292	7	channel	channel	NOUN
ajst-25954	292	8	attention	attention	NOUN
ajst-25954	292	9	dual	dual	ADJ
ajst-25954	292	10	input	input	NOUN
ajst-25954	292	11	convolutional	convolutional	ADJ
ajst-25954	292	12	neural	neural	ADJ
ajst-25954	292	13	network	network	NOUN
ajst-25954	292	14	.	.	PUNCT
ajst-25954	293	1	physica	physica	PROPN
ajst-25954	293	2	a	a	DET
ajst-25954	293	3	:	:	PUNCT
ajst-25954	293	4	statistical	statistical	ADJ
ajst-25954	293	5	mechanics	mechanic	NOUN
ajst-25954	293	6	and	and	CCONJ
ajst-25954	293	7	its	its	PRON
ajst-25954	293	8	applications	application	NOUN
ajst-25954	293	9	,	,	PUNCT
ajst-25954	293	10	2021.584	2021.584	NUM
ajst-25954	293	11	:	:	PUNCT
ajst-25954	293	12	p.126376	p.126376	NOUN
ajst-25954	293	13	.	.	PUNCT
ajst-25954	294	1	[	[	X
ajst-25954	294	2	17	17	NUM
ajst-25954	294	3	]	]	PUNCT
ajst-25954	294	4	t.	t.	PROPN
ajst-25954	294	5	zhang	zhang	PROPN
ajst-25954	294	6	,	,	PUNCT
ajst-25954	294	7	x.	x.	PROPN
ajst-25954	294	8	wang	wang	PROPN
ajst-25954	294	9	,	,	PUNCT
ajst-25954	294	10	x.	x.	PROPN
ajst-25954	294	11	xu	xu	PROPN
ajst-25954	294	12	,	,	PUNCT
ajst-25954	294	13	and	and	CCONJ
ajst-25954	294	14	c.	c.	PROPN
ajst-25954	294	15	l.	l.	PROPN
ajst-25954	294	16	p.	p.	PROPN
ajst-25954	294	17	chen	chen	PROPN
ajst-25954	294	18	,	,	PUNCT
ajst-25954	294	19	“	"	PUNCT
ajst-25954	294	20	gcb	gcb	PROPN
ajst-25954	294	21	-	-	NOUN
ajst-25954	294	22	net	net	NOUN
ajst-25954	294	23	:	:	PUNCT
ajst-25954	294	24	graph	graph	VERB
ajst-25954	294	25	convolutional	convolutional	ADJ
ajst-25954	294	26	broad	broad	ADJ
ajst-25954	294	27	network	network	NOUN
ajst-25954	294	28	and	and	CCONJ
ajst-25954	294	29	its	its	PRON
ajst-25954	294	30	application	application	NOUN
ajst-25954	294	31	in	in	ADP
ajst-25954	294	32	emotion	emotion	NOUN
ajst-25954	294	33	recognition	recognition	NOUN
ajst-25954	294	34	”	"	PUNCT
ajst-25954	294	35	ieee	ieee	PROPN
ajst-25954	294	36	trans	tran	NOUN
ajst-25954	294	37	.	.	PROPN
ajst-25954	295	1	affect	affect	VERB
ajst-25954	295	2	.	.	PUNCT
ajst-25954	296	1	comput	comput	NOUN
ajst-25954	296	2	.	.	PUNCT
ajst-25954	296	3	,	,	PUNCT
ajst-25954	296	4	vol	vol	NOUN
ajst-25954	296	5	.	.	PROPN
ajst-25954	296	6	13	13	NUM
ajst-25954	296	7	,	,	PUNCT
ajst-25954	296	8	no	no	INTJ
ajst-25954	296	9	.	.	NOUN
ajst-25954	296	10	1	1	NUM
ajst-25954	296	11	,	,	PUNCT
ajst-25954	296	12	pp	pp	ADJ
ajst-25954	296	13	.	.	PUNCT
ajst-25954	297	1	379–388,jan	379–388,jan	NUM
ajst-25954	297	2	.	.	NOUN
ajst-25954	297	3	2022	2022	NUM
ajst-25954	297	4	.	.	PUNCT
ajst-25954	298	1	[	[	X
ajst-25954	298	2	18	18	NUM
ajst-25954	298	3	]	]	X
ajst-25954	298	4	q.	q.	PROPN
ajst-25954	298	5	lian	lian	PROPN
ajst-25954	298	6	,	,	PUNCT
ajst-25954	298	7	y.	y.	PROPN
ajst-25954	298	8	qi	qi	PROPN
ajst-25954	298	9	,	,	PUNCT
ajst-25954	298	10	g.	g.	PROPN
ajst-25954	298	11	pan	pan	PROPN
ajst-25954	298	12	,	,	PUNCT
ajst-25954	298	13	and	and	CCONJ
ajst-25954	298	14	y.	y.	PROPN
ajst-25954	298	15	wang	wang	PROPN
ajst-25954	298	16	,	,	PUNCT
ajst-25954	298	17	“	"	PUNCT
ajst-25954	298	18	learning	learn	VERB
ajst-25954	298	19	graph	graph	NOUN
ajst-25954	298	20	in	in	ADP
ajst-25954	298	21	graph	graph	NOUN
ajst-25954	298	22	convolutional	convolutional	ADJ
ajst-25954	298	23	neural	neural	ADJ
ajst-25954	298	24	networks	network	NOUN
ajst-25954	298	25	for	for	ADP
ajst-25954	298	26	robust	robust	ADJ
ajst-25954	298	27	seizure	seizure	NOUN
ajst-25954	298	28	prediction	prediction	NOUN
ajst-25954	298	29	,	,	PUNCT
ajst-25954	298	30	”	"	PUNCT
ajst-25954	298	31	j.	j.	PROPN
ajst-25954	298	32	neural	neural	PROPN
ajst-25954	298	33	eng	eng	PROPN
ajst-25954	298	34	.	.	PROPN
ajst-25954	298	35	,	,	PUNCT
ajst-25954	298	36	vol	vol	NOUN
ajst-25954	298	37	.	.	PUNCT
ajst-25954	298	38	17,no	17,no	X
ajst-25954	298	39	.	.	NOUN
ajst-25954	298	40	3	3	NUM
ajst-25954	298	41	,	,	PUNCT
ajst-25954	298	42	jun	jun	PROPN
ajst-25954	298	43	.	.	PROPN
ajst-25954	298	44	2020	2020	NUM
ajst-25954	298	45	,	,	PUNCT
ajst-25954	298	46	art	art	NOUN
ajst-25954	298	47	.	.	PUNCT
ajst-25954	299	1	no	no	INTJ
ajst-25954	299	2	.	.	PUNCT
ajst-25954	299	3	035004	035004	NUM
ajst-25954	299	4	.	.	PUNCT
ajst-25954	300	1	[	[	X
ajst-25954	300	2	19	19	NUM
ajst-25954	300	3	]	]	PUNCT
ajst-25954	300	4	t.dissanayake	t.dissanayake	NOUN
ajst-25954	300	5	,	,	PUNCT
ajst-25954	300	6	t.	t.	NOUN
ajst-25954	300	7	fernando	fernando	PROPN
ajst-25954	300	8	,	,	PUNCT
ajst-25954	300	9	s.	s.	PROPN
ajst-25954	300	10	denman	denman	PROPN
ajst-25954	300	11	,	,	PUNCT
ajst-25954	300	12	s.	s.	PROPN
ajst-25954	300	13	sridharan	sridharan	ADJ
ajst-25954	300	14	,	,	PUNCT
ajst-25954	300	15	and	and	CCONJ
ajst-25954	300	16	c.	c.	PROPN
ajst-25954	300	17	fookes,“geometric	fookes,“geometric	PROPN
ajst-25954	300	18	deep	deep	ADJ
ajst-25954	300	19	learning	learning	NOUN
ajst-25954	300	20	for	for	ADP
ajst-25954	300	21	subject	subject	ADJ
ajst-25954	300	22	independent	independent	ADJ
ajst-25954	300	23	epileptic	epileptic	ADJ
ajst-25954	300	24	seizure	seizure	NOUN
ajst-25954	300	25	prediction	prediction	NOUN
ajst-25954	300	26	using	use	VERB
ajst-25954	300	27	scalp	scalp	NOUN
ajst-25954	300	28	eeg	eeg	NOUN
ajst-25954	300	29	signals	signal	NOUN
ajst-25954	300	30	,	,	PUNCT
ajst-25954	300	31	”	"	PUNCT
ajst-25954	300	32	ieee	ieee	PROPN
ajst-25954	300	33	j.	j.	PROPN
ajst-25954	300	34	biomed	biome	VERB
ajst-25954	300	35	.	.	PUNCT
ajst-25954	301	1	health	health	NOUN
ajst-25954	301	2	informat	informat	NOUN
ajst-25954	301	3	.	.	PUNCT
ajst-25954	302	1	,vol	,vol	PROPN
ajst-25954	302	2	.	.	PUNCT
ajst-25954	303	1	26	26	NUM
ajst-25954	303	2	,	,	PUNCT
ajst-25954	303	3	no	no	INTJ
ajst-25954	303	4	.	.	PUNCT
ajst-25954	304	1	2,pp	2,pp	X
ajst-25954	304	2	.	.	PUNCT
ajst-25954	305	1	527–538	527–538	NUM
ajst-25954	305	2	,	,	PUNCT
ajst-25954	305	3	feb	feb	PROPN
ajst-25954	305	4	.	.	PROPN
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ajst-25954	305	6	.	.	PUNCT
ajst-25954	306	1	[	[	X
ajst-25954	306	2	20	20	NUM
ajst-25954	306	3	]	]	PUNCT
ajst-25954	306	4	s.	s.	PROPN
ajst-25954	306	5	jang	jang	PROPN
ajst-25954	306	6	,	,	PUNCT
ajst-25954	306	7	s.e	s.e	PROPN
ajst-25954	306	8	.	.	PROPN
ajst-25954	306	9	moon	moon	PROPN
ajst-25954	306	10	,	,	PUNCT
ajst-25954	306	11	and	and	CCONJ
ajst-25954	306	12	j.s	j.s	PROPN
ajst-25954	306	13	.	.	PROPN
ajst-25954	306	14	lee	lee	PROPN
ajst-25954	306	15	,	,	PUNCT
ajst-25954	306	16	“	"	PUNCT
ajst-25954	306	17	eeg	eeg	NOUN
ajst-25954	306	18	-	-	PUNCT
ajst-25954	306	19	based	base	VERB
ajst-25954	306	20	video	video	NOUN
ajst-25954	306	21	identification	identification	NOUN
ajst-25954	306	22	using	use	VERB
ajst-25954	306	23	graph	graph	NOUN
ajst-25954	306	24	signal	signal	NOUN
ajst-25954	306	25	modeling	modeling	NOUN
ajst-25954	306	26	and	and	CCONJ
ajst-25954	306	27	graph	graph	VERB
ajst-25954	306	28	convolutional	convolutional	ADJ
ajst-25954	306	29	neural	neural	ADJ
ajst-25954	306	30	network	network	NOUN
ajst-25954	306	31	,	,	PUNCT
ajst-25954	306	32	”	"	PUNCT
ajst-25954	306	33	in	in	ADP
ajst-25954	306	34	proc	proc	NOUN
ajst-25954	306	35	.	.	PUNCT
ajst-25954	307	1	ieee	ieee	PROPN
ajst-25954	307	2	int	int	PROPN
ajst-25954	307	3	.	.	PUNCT
ajst-25954	308	1	conf	conf	PROPN
ajst-25954	308	2	.	.	PUNCT
ajst-25954	309	1	acoust	acoust	PROPN
ajst-25954	309	2	.	.	PUNCT
ajst-25954	310	1	,	,	PUNCT
ajst-25954	310	2	speech	speech	NOUN
ajst-25954	310	3	signal	signal	NOUN
ajst-25954	310	4	process	process	NOUN
ajst-25954	310	5	.	.	PUNCT
ajst-25954	311	1	(	(	PUNCT
ajst-25954	311	2	icassp),apr	icassp),apr	PROPN
ajst-25954	311	3	.	.	PUNCT
ajst-25954	312	1	2018	2018	NUM
ajst-25954	312	2	,	,	PUNCT
ajst-25954	312	3	pp	pp	ADJ
ajst-25954	312	4	.	.	PUNCT
ajst-25954	313	1	3066–3070	3066–3070	NUM
ajst-25954	313	2	.	.	PUNCT
ajst-25954	314	1	[	[	X
ajst-25954	314	2	21	21	NUM
ajst-25954	314	3	]	]	PUNCT
ajst-25954	314	4	p.	p.	PROPN
ajst-25954	314	5	zhong	zhong	PROPN
ajst-25954	314	6	,	,	PUNCT
ajst-25954	314	7	d.	d.	PROPN
ajst-25954	314	8	wang	wang	PROPN
ajst-25954	314	9	,	,	PUNCT
ajst-25954	314	10	and	and	CCONJ
ajst-25954	314	11	c.	c.	PROPN
ajst-25954	314	12	miao	miao	PROPN
ajst-25954	314	13	,	,	PUNCT
ajst-25954	314	14	“	"	PUNCT
ajst-25954	314	15	eeg	eeg	NOUN
ajst-25954	314	16	-	-	PUNCT
ajst-25954	314	17	based	base	VERB
ajst-25954	314	18	emotion	emotion	NOUN
ajst-25954	314	19	recognition	recognition	NOUN
ajst-25954	314	20	using	use	VERB
ajst-25954	314	21	regularized	regularize	VERB
ajst-25954	314	22	graph	graph	NOUN
ajst-25954	314	23	neural	neural	ADJ
ajst-25954	314	24	networks	network	NOUN
ajst-25954	314	25	,	,	PUNCT
ajst-25954	314	26	”	"	PUNCT
ajst-25954	314	27	ieee	ieee	NOUN
ajst-25954	314	28	trans	tran	NOUN
ajst-25954	314	29	.	.	PROPN
ajst-25954	314	30	affect	affect	VERB
ajst-25954	314	31	.	.	PUNCT
ajst-25954	315	1	comput	comput	NOUN
ajst-25954	315	2	.	.	PUNCT
ajst-25954	316	1	,vol.13	,vol.13	PUNCT
ajst-25954	316	2	,	,	PUNCT
ajst-25954	316	3	no.3	no.3	PROPN
ajst-25954	316	4	,	,	PUNCT
ajst-25954	316	5	pp.1290–1301	pp.1290–1301	PROPN
ajst-25954	316	6	,	,	PUNCT
ajst-25954	316	7	jul	jul	PROPN
ajst-25954	316	8	.	.	PROPN
ajst-25954	316	9	2022	2022	NUM
ajst-25954	316	10	.	.	PUNCT
ajst-25954	317	1	[	[	X
ajst-25954	317	2	22	22	NUM
ajst-25954	317	3	]	]	X
ajst-25954	317	4	y.	y.	PROPN
ajst-25954	317	5	li	li	PROPN
ajst-25954	317	6	,	,	PUNCT
ajst-25954	317	7	y.	y.	PROPN
ajst-25954	317	8	liu	liu	PROPN
ajst-25954	317	9	,	,	PUNCT
ajst-25954	317	10	w.g	w.g	PROPN
ajst-25954	317	11	.	.	PROPN
ajst-25954	317	12	cui	cui	PROPN
ajst-25954	317	13	,	,	PUNCT
ajst-25954	317	14	y.z	y.z	PROPN
ajst-25954	317	15	.	.	PROPN
ajst-25954	317	16	guo	guo	PROPN
ajst-25954	317	17	,	,	PUNCT
ajst-25954	317	18	h.	h.	PROPN
ajst-25954	317	19	huang	huang	PROPN
ajst-25954	317	20	,	,	PUNCT
ajst-25954	317	21	and	and	CCONJ
ajst-25954	317	22	z.y	z.y	PROPN
ajst-25954	317	23	.	.	PROPN
ajst-25954	317	24	hu	hu	PROPN
ajst-25954	317	25	.	.	PROPN
ajst-25954	317	26	,	,	PUNCT
ajst-25954	317	27	“	"	PUNCT
ajst-25954	317	28	epileptic	epileptic	ADJ
ajst-25954	317	29	seizure	seizure	NOUN
ajst-25954	317	30	detection	detection	NOUN
ajst-25954	317	31	in	in	ADP
ajst-25954	317	32	eeg	eeg	NOUN
ajst-25954	317	33	signals	signal	NOUN
ajst-25954	317	34	using	use	VERB
ajst-25954	317	35	a	a	DET
ajst-25954	317	36	unified	unified	ADJ
ajst-25954	317	37	temporal	temporal	ADJ
ajst-25954	317	38	-	-	PUNCT
ajst-25954	317	39	spectral	spectral	ADJ
ajst-25954	317	40	squeeze	squeeze	NOUN
ajst-25954	317	41	-	-	PUNCT
ajst-25954	317	42	and	and	CCONJ
ajst-25954	317	43	-	-	PUNCT
ajst-25954	317	44	excitation	excitation	NOUN
ajst-25954	317	45	network	network	NOUN
ajst-25954	317	46	,	,	PUNCT
ajst-25954	317	47	”	"	PUNCT
ajst-25954	317	48	ieee	ieee	NOUN
ajst-25954	317	49	trans	trans	PROPN
ajst-25954	317	50	.	.	PUNCT
ajst-25954	317	51	neural	neural	ADJ
ajst-25954	317	52	syst	syst	PROPN
ajst-25954	317	53	.	.	PUNCT
ajst-25954	318	1	rehabil	rehabil	PROPN
ajst-25954	318	2	.	.	PUNCT
ajst-25954	319	1	eng	eng	PROPN
ajst-25954	319	2	.	.	PUNCT
ajst-25954	319	3	,vol	,vol	PROPN
ajst-25954	319	4	.	.	PUNCT
ajst-25954	320	1	28	28	NUM
ajst-25954	320	2	,	,	PUNCT
ajst-25954	320	3	no	no	INTJ
ajst-25954	320	4	.	.	NOUN
ajst-25954	320	5	4	4	NUM
ajst-25954	320	6	,	,	PUNCT
ajst-25954	320	7	pp	pp	ADJ
ajst-25954	320	8	.	.	PUNCT
ajst-25954	321	1	782–794	782–794	NUM
ajst-25954	321	2	,	,	PUNCT
ajst-25954	321	3	apr	apr	PROPN
ajst-25954	321	4	.	.	PUNCT
ajst-25954	321	5	2020	2020	NUM
ajst-25954	321	6	.	.	PUNCT
ajst-25954	322	1	[	[	X
ajst-25954	322	2	23	23	NUM
ajst-25954	322	3	]	]	X
ajst-25954	322	4	prathaban	prathaban	PROPN
ajst-25954	322	5	,	,	PUNCT
ajst-25954	322	6	b.	b.	PROPN
ajst-25954	322	7	p.	p.	PROPN
ajst-25954	322	8	,	,	PUNCT
ajst-25954	322	9	and	and	CCONJ
ajst-25954	322	10	balasubramanian	balasubramanian	PROPN
ajst-25954	322	11	,	,	PUNCT
ajst-25954	322	12	r.	r.	PROPN
ajst-25954	322	13	,	,	PUNCT
ajst-25954	322	14	“	"	PUNCT
ajst-25954	322	15	dynam	dynam	PROPN
ajst-25954	322	16	-	-	PUNCT
ajst-25954	322	17	ic	ic	NOUN
ajst-25954	322	18	learning	learn	VERB
ajst-25954	322	19	framework	framework	NOUN
ajst-25954	322	20	for	for	ADP
ajst-25954	322	21	epileptic	epileptic	ADJ
ajst-25954	322	22	seizure	seizure	NOUN
ajst-25954	322	23	prediction	prediction	NOUN
ajst-25954	322	24	usi	usi	PROPN
ajst-25954	322	25	-	-	PUNCT
ajst-25954	322	26	ng	ng	PROPN
ajst-25954	322	27	sparsity	sparsity	NOUN
ajst-25954	322	28	based	base	VERB
ajst-25954	322	29	eeg	eeg	NOUN
ajst-25954	322	30	reconstruction	reconstruction	NOUN
ajst-25954	322	31	with	with	ADP
ajst-25954	322	32	optimized	optimize	VERB
ajst-25954	322	33	c	c	PROPN
ajst-25954	322	34	-	-	PUNCT
ajst-25954	322	35	nn	nn	X
ajst-25954	322	36	classifier	classifier	NOUN
ajst-25954	322	37	”	"	PUNCT
ajst-25954	322	38	.	.	PUNCT
ajst-25954	323	1	expert	expert	PROPN
ajst-25954	323	2	syst	syst	PROPN
ajst-25954	323	3	.	.	PUNCT
ajst-25954	324	1	appl	appl	PROPN
ajst-25954	324	2	.	.	PUNCT
ajst-25954	325	1	170:114533.doi	170:114533.doi	NUM
ajst-25954	325	2	:	:	PUNCT
ajst-25954	325	3	10.1016	10.1016	NUM
ajst-25954	325	4	/	/	SYM
ajst-25954	325	5	j.eswa.2020.114533	j.eswa.2020.114533	ADJ
ajst-25954	325	6	.	.	PUNCT
ajst-25954	326	1	[	[	X
ajst-25954	326	2	24	24	NUM
ajst-25954	326	3	]	]	X
ajst-25954	326	4	chao	chao	PROPN
ajst-25954	326	5	h	h	PROPN
ajst-25954	326	6	,	,	PUNCT
ajst-25954	326	7	dong	dong	PROPN
ajst-25954	326	8	l.	l.	PROPN
ajst-25954	326	9	emotion	emotion	PROPN
ajst-25954	326	10	recognition	recognition	NOUN
ajst-25954	326	11	using	use	VERB
ajst-25954	326	12	threedimensional	threedimensional	ADJ
ajst-25954	326	13	feature	feature	NOUN
ajst-25954	326	14	and	and	CCONJ
ajst-25954	326	15	convolutional	convolutional	ADJ
ajst-25954	326	16	neural	neural	ADJ
ajst-25954	326	17	network	network	NOUN
ajst-25954	326	18	from	from	ADP
ajst-25954	326	19	multichannel	multichannel	NOUN
ajst-25954	326	20	eeg	eeg	NOUN
ajst-25954	326	21	signals	signal	NOUN
ajst-25954	326	22	.	.	PUNCT
ajst-25954	327	1	ieee	ieee	NOUN
ajst-25954	327	2	sensors	sensor	NOUN
ajst-25954	327	3	journal	journal	NOUN
ajst-25954	327	4	,	,	PUNCT
ajst-25954	327	5	2021	2021	NUM
ajst-25954	327	6	.	.	PUNCT
ajst-25954	328	1	21(2	21(2	NUM
ajst-25954	328	2	):	):	PUNCT
ajst-25954	328	3	2024	2024	NUM
ajst-25954	328	4	-	-	SYM
ajst-25954	328	5	2034	2034	NUM
ajst-25954	328	6	.	.	PUNCT
ajst-25954	329	1	[	[	X
ajst-25954	329	2	25	25	NUM
ajst-25954	329	3	]	]	X
ajst-25954	329	4	rendle	rendle	PROPN
ajst-25954	329	5	,	,	PUNCT
ajst-25954	329	6	steffen	steffen	PROPN
ajst-25954	329	7	,	,	PUNCT
ajst-25954	329	8	2010	2010	NUM
ajst-25954	329	9	.	.	PUNCT
ajst-25954	330	1	factorization	factorization	NOUN
ajst-25954	330	2	machines	machine	NOUN
ajst-25954	330	3	.	.	PUNCT
ajst-25954	331	1	in	in	ADP
ajst-25954	331	2	:	:	PUNCT
ajst-25954	331	3	2010	2010	NUM
ajst-25954	331	4	ieee	ieee	PROPN
ajst-25954	331	5	international	international	ADJ
ajst-25954	331	6	conference	conference	NOUN
ajst-25954	331	7	on	on	ADP
ajst-25954	331	8	data	datum	NOUN
ajst-25954	331	9	mining	mining	NOUN
ajst-25954	331	10	.	.	PUNCT
ajst-25954	332	1	ieee	ieee	PROPN
ajst-25954	332	2	,	,	PUNCT
ajst-25954	332	3	pp.995–1000	pp.995–1000	PROPN
ajst-25954	332	4	.	.	PUNCT
ajst-25954	333	1	[	[	X
ajst-25954	333	2	online	online	X
ajst-25954	333	3	]	]	X
ajst-25954	333	4	available	available	ADJ
ajst-25954	333	5	:	:	PUNCT
ajst-25954	333	6	http://dx.doi.org/10.1109/icdm.2010.127	http://dx.doi.org/10.1109/icdm.2010.127	NUM
ajst-25954	333	7	.	.	PUNCT
ajst-25954	334	1	[	[	X
ajst-25954	334	2	26	26	NUM
ajst-25954	334	3	]	]	X
ajst-25954	334	4	gao	gao	PROPN
ajst-25954	334	5	,	,	PUNCT
ajst-25954	334	6	y.y	y.y	PROPN
ajst-25954	334	7	.	.	PROPN
ajst-25954	334	8	,	,	PUNCT
ajst-25954	334	9	et	et	PROPN
ajst-25954	334	10	al	al	PROPN
ajst-25954	334	11	.	.	PROPN
ajst-25954	334	12	,	,	PUNCT
ajst-25954	334	13	“	"	PUNCT
ajst-25954	334	14	deep	deep	ADJ
ajst-25954	334	15	convolutional	convolutional	ADJ
ajst-25954	334	16	neural	neural	ADJ
ajst-25954	334	17	network	network	NOUN
ajst-25954	334	18	based	base	VERB
ajst-25954	334	19	epileptic	epileptic	ADJ
ajst-25954	334	20	electroencephalogram	electroencephalogram	NOUN
ajst-25954	334	21	(	(	PUNCT
ajst-25954	334	22	eeg	eeg	NOUN
ajst-25954	334	23	)	)	PUNCT
ajst-25954	334	24	signal	signal	NOUN
ajst-25954	334	25	classification”.frontiers	classification”.frontier	NOUN
ajst-25954	334	26	in	in	ADP
ajst-25954	334	27	neurology	neurology	NOUN
ajst-25954	334	28	,	,	PUNCT
ajst-25954	334	29	2020	2020	NUM
ajst-25954	334	30	.	.	PUNCT
ajst-25954	335	1	11	11	NUM
ajst-25954	335	2	.	.	PUNCT
ajst-25954	336	1	[	[	X
ajst-25954	336	2	27	27	NUM
ajst-25954	336	3	]	]	PUNCT
ajst-25954	336	4	a.	a.	PROPN
ajst-25954	336	5	r.	r.	PROPN
ajst-25954	336	6	ozcan	ozcan	PROPN
ajst-25954	336	7	and	and	CCONJ
ajst-25954	336	8	s.	s.	PROPN
ajst-25954	336	9	erturk	erturk	PROPN
ajst-25954	336	10	,	,	PUNCT
ajst-25954	336	11	“	"	PUNCT
ajst-25954	336	12	seizure	seizure	VERB
ajst-25954	336	13	prediction	prediction	NOUN
ajst-25954	336	14	in	in	ADP
ajst-25954	336	15	scalp	scalp	NOUN
ajst-25954	336	16	eeg	eeg	NOUN
ajst-25954	336	17	using	use	VERB
ajst-25954	336	18	3d	3d	NUM
ajst-25954	336	19	convolutional	convolutional	ADJ
ajst-25954	336	20	neural	neural	ADJ
ajst-25954	336	21	networks	network	NOUN
ajst-25954	336	22	with	with	ADP
ajst-25954	336	23	an	an	DET
ajst-25954	336	24	image	image	NOUN
ajst-25954	336	25	-	-	PUNCT
ajst-25954	336	26	based	base	VERB
ajst-25954	336	27	approach	approach	NOUN
ajst-25954	336	28	,	,	PUNCT
ajst-25954	336	29	”	"	PUNCT
ajst-25954	336	30	ieee	ieee	NOUN
ajst-25954	336	31	trans	trans	PROPN
ajst-25954	336	32	.	.	PUNCT
ajst-25954	337	1	neural	neural	ADJ
ajst-25954	337	2	syst	syst	PROPN
ajst-25954	337	3	.	.	PUNCT
ajst-25954	338	1	rehabil	rehabil	PROPN
ajst-25954	338	2	.	.	PUNCT
ajst-25954	339	1	eng	eng	PROPN
ajst-25954	339	2	.	.	PROPN
ajst-25954	339	3	,	,	PUNCT
ajst-25954	339	4	vol	vol	NOUN
ajst-25954	339	5	.	.	PROPN
ajst-25954	340	1	27	27	NUM
ajst-25954	340	2	,	,	PUNCT
ajst-25954	340	3	no	no	INTJ
ajst-25954	340	4	.	.	NOUN
ajst-25954	340	5	11	11	NUM
ajst-25954	340	6	,	,	PUNCT
ajst-25954	340	7	pp	pp	ADJ
ajst-25954	340	8	.	.	PUNCT
ajst-25954	341	1	2284–2293,nov	2284–2293,nov	NUM
ajst-25954	341	2	.	.	NOUN
ajst-25954	341	3	2019	2019	NUM
ajst-25954	341	4	.	.	PUNCT
ajst-25954	342	1	[	[	X
ajst-25954	342	2	28	28	NUM
ajst-25954	342	3	]	]	X
ajst-25954	342	4	d.	d.	PROPN
ajst-25954	342	5	r.	r.	PROPN
ajst-25954	342	6	freestone	freestone	PROPN
ajst-25954	342	7	,	,	PUNCT
ajst-25954	342	8	p.	p.	PROPN
ajst-25954	342	9	j.	j.	PROPN
ajst-25954	342	10	karoly	karoly	PROPN
ajst-25954	342	11	,	,	PUNCT
ajst-25954	342	12	and	and	CCONJ
ajst-25954	342	13	m.	m.	PROPN
ajst-25954	342	14	j.	j.	PROPN
ajst-25954	342	15	cook	cook	PROPN
ajst-25954	342	16	,	,	PUNCT
ajst-25954	342	17	‘	'	PUNCT
ajst-25954	342	18	‘	'	PUNCT
ajst-25954	342	19	a	a	DET
ajst-25954	342	20	forward	forward	ADV
ajst-25954	342	21	looking	look	VERB
ajst-25954	342	22	review	review	NOUN
ajst-25954	342	23	of	of	ADP
ajst-25954	342	24	seizure	seizure	NOUN
ajst-25954	342	25	prediction	prediction	NOUN
ajst-25954	342	26	,	,	PUNCT
ajst-25954	342	27	’’	’'	PUNCT
ajst-25954	342	28	current	current	ADJ
ajst-25954	342	29	opinion	opinion	NOUN
ajst-25954	342	30	neurol	neurol	NOUN
ajst-25954	342	31	.	.	PUNCT
ajst-25954	342	32	,	,	PUNCT
ajst-25954	342	33	vol.30	vol.30	NOUN
ajst-25954	342	34	,	,	PUNCT
ajst-25954	342	35	no	no	INTJ
ajst-25954	342	36	.	.	NOUN
ajst-25954	342	37	2	2	NUM
ajst-25954	342	38	,	,	PUNCT
ajst-25954	342	39	pp.167–173	pp.167–173	NOUN
ajst-25954	342	40	,	,	PUNCT
ajst-25954	342	41	2017	2017	NUM
ajst-25954	342	42	.	.	PUNCT
ajst-25954	343	1	[	[	X
ajst-25954	343	2	29	29	NUM
ajst-25954	343	3	]	]	X
ajst-25954	343	4	y.	y.	PROPN
ajst-25954	343	5	li	li	PROPN
ajst-25954	343	6	,	,	PUNCT
ajst-25954	343	7	y.	y.	PROPN
ajst-25954	343	8	liu	liu	PROPN
ajst-25954	343	9	,	,	PUNCT
ajst-25954	343	10	y.z	y.z	PROPN
ajst-25954	343	11	.	.	PROPN
ajst-25954	343	12	guo	guo	PROPN
ajst-25954	343	13	,	,	PUNCT
ajst-25954	343	14	x.f	x.f	PROPN
ajst-25954	343	15	.	.	PROPN
ajst-25954	343	16	liao	liao	PROPN
ajst-25954	343	17	,	,	PUNCT
ajst-25954	343	18	b.	b.	PROPN
ajst-25954	343	19	hu	hu	PROPN
ajst-25954	343	20	,	,	PUNCT
ajst-25954	343	21	and	and	CCONJ
ajst-25954	343	22	t.	t.	PROPN
ajst-25954	343	23	yu,“spatiotemporal	yu,“spatiotemporal	PROPN
ajst-25954	343	24	-	-	PUNCT
ajst-25954	343	25	spectral	spectral	ADJ
ajst-25954	343	26	hierarchical	hierarchical	ADJ
ajst-25954	343	27	graph	graph	NOUN
ajst-25954	343	28	convolutional	convolutional	ADJ
ajst-25954	343	29	network	network	NOUN
ajst-25954	343	30	with	with	ADP
ajst-25954	343	31	semi	semi	ADJ
ajst-25954	343	32	-	-	ADJ
ajst-25954	343	33	supervised	supervised	ADJ
ajst-25954	343	34	active	active	ADJ
ajst-25954	343	35	learning	learning	NOUN
ajst-25954	343	36	for	for	ADP
ajst-25954	343	37	patie	patie	NOUN
ajst-25954	343	38	-	-	PUNCT
ajst-25954	343	39	nt	not	PART
ajst-25954	343	40	specific	specific	ADJ
ajst-25954	343	41	seizure	seizure	NOUN
ajst-25954	343	42	prediction	prediction	NOUN
ajst-25954	343	43	,	,	PUNCT
ajst-25954	343	44	”	"	PUNCT
ajst-25954	343	45	ieee	ieee	NOUN
ajst-25954	343	46	trans	tran	NOUN
ajst-25954	343	47	.	.	PUNCT
ajst-25954	344	1	cybern	cybern	PROPN
ajst-25954	344	2	.	.	PROPN
ajst-25954	344	3	,	,	PUNCT
ajst-25954	344	4	vol.52	vol.52	NOUN
ajst-25954	344	5	,	,	PUNCT
ajst-25954	344	6	no.11	no.11	NOUN
ajst-25954	344	7	,	,	PUNCT
ajst-25954	344	8	pp	pp	ADJ
ajst-25954	344	9	.	.	PUNCT
ajst-25954	345	1	12189–12204,nov	12189–12204,nov	X
ajst-25954	345	2	.	.	NOUN
ajst-25954	345	3	2022	2022	NUM
ajst-25954	345	4	.	.	PUNCT
ajst-25954	346	1	[	[	X
ajst-25954	346	2	30	30	NUM
ajst-25954	346	3	]	]	X
ajst-25954	346	4	n.	n.	PROPN
ajst-25954	346	5	d.	d.	PROPN
ajst-25954	346	6	truong	truong	PROPN
ajst-25954	346	7	et	et	PROPN
ajst-25954	346	8	al	al	PROPN
ajst-25954	346	9	.	.	PROPN
ajst-25954	346	10	,	,	PUNCT
ajst-25954	346	11	“	"	PUNCT
ajst-25954	346	12	convolutional	convolutional	ADJ
ajst-25954	346	13	neural	neural	ADJ
ajst-25954	346	14	networks	network	NOUN
ajst-25954	346	15	for	for	ADP
ajst-25954	346	16	seizure	seizure	NOUN
ajst-25954	346	17	prediction	prediction	NOUN
ajst-25954	346	18	using	use	VERB
ajst-25954	346	19	intracranial	intracranial	ADJ
ajst-25954	346	20	and	and	CCONJ
ajst-25954	346	21	scalp	scalp	NOUN
ajst-25954	346	22	electroencephalogram	electroencephalogram	NOUN
ajst-25954	346	23	,	,	PUNCT
ajst-25954	346	24	”	"	PUNCT
ajst-25954	346	25	neural	neural	ADJ
ajst-25954	346	26	netw	netw	NOUN
ajst-25954	346	27	.	.	PUNCT
ajst-25954	346	28	,	,	PUNCT
ajst-25954	346	29	vol	vol	NOUN
ajst-25954	346	30	.	.	PROPN
ajst-25954	346	31	105	105	NUM
ajst-25954	346	32	,	,	PUNCT
ajst-25954	346	33	pp	pp	ADJ
ajst-25954	346	34	.	.	PUNCT
ajst-25954	347	1	104–111	104–111	NUM
ajst-25954	347	2	,	,	PUNCT
ajst-25954	347	3	sep	sep	PROPN
ajst-25954	347	4	.	.	PROPN
ajst-25954	347	5	2018	2018	NUM
ajst-25954	347	6	.	.	PUNCT
ajst-25954	348	1	[	[	X
ajst-25954	348	2	31	31	NUM
ajst-25954	348	3	]	]	X
ajst-25954	348	4	ma	ma	PROPN
ajst-25954	348	5	,	,	PUNCT
ajst-25954	348	6	y.	y.	PROPN
ajst-25954	348	7	,	,	PUNCT
ajst-25954	348	8	et	et	PROPN
ajst-25954	348	9	al	al	PROPN
ajst-25954	348	10	.	.	PROPN
ajst-25954	348	11	,	,	PUNCT
ajst-25954	348	12	a	a	DET
ajst-25954	348	13	multi	multi	ADJ
ajst-25954	348	14	-	-	ADJ
ajst-25954	348	15	channel	channel	ADJ
ajst-25954	348	16	feature	feature	NOUN
ajst-25954	348	17	fusion	fusion	NOUN
ajst-25954	348	18	cnn	cnn	PROPN
ajst-25954	348	19	-	-	PUNCT
ajst-25954	348	20	bi	bi	ADJ
ajst-25954	348	21	-	-	ADJ
ajst-25954	348	22	lstm	lstm	ADJ
ajst-25954	348	23	epilepsy	epilepsy	NOUN
ajst-25954	348	24	eeg	eeg	NOUN
ajst-25954	348	25	classification	classification	NOUN
ajst-25954	348	26	and	and	CCONJ
ajst-25954	348	27	prediction	prediction	NOUN
ajst-25954	348	28	model	model	NOUN
ajst-25954	348	29	based	base	VERB
ajst-25954	348	30	on	on	ADP
ajst-25954	348	31	attention	attention	NOUN
ajst-25954	348	32	mechanism	mechanism	NOUN
ajst-25954	348	33	.	.	PUNCT
ajst-25954	349	1	ieee	ieee	NOUN
ajst-25954	349	2	access	access	NOUN
ajst-25954	349	3	,	,	PUNCT
ajst-25954	349	4	2023	2023	NUM
ajst-25954	349	5	.	.	PUNCT
ajst-25954	350	1	11	11	NUM
ajst-25954	350	2	:	:	PUNCT
ajst-25954	351	1	p.	p.	NOUN
ajst-25954	351	2	6285562864	6285562864	NUM
ajst-25954	351	3	.	.	PUNCT
ajst-25954	352	1	[	[	X
ajst-25954	352	2	32	32	NUM
ajst-25954	352	3	]	]	X
ajst-25954	352	4	zhong	zhong	PROPN
ajst-25954	352	5	,	,	PUNCT
ajst-25954	352	6	l.	l.	PROPN
ajst-25954	352	7	,et	,et	PUNCT
ajst-25954	352	8	al	al	PROPN
ajst-25954	352	9	.	.	PROPN
ajst-25954	352	10	,	,	PUNCT
ajst-25954	352	11	epileptic	epileptic	ADJ
ajst-25954	352	12	prediction	prediction	NOUN
ajst-25954	352	13	using	use	VERB
ajst-25954	352	14	spatiotem	spatiotem	NOUN
ajst-25954	352	15	-	-	PUNCT
ajst-25954	352	16	poral	poral	ADJ
ajst-25954	352	17	information	information	NOUN
ajst-25954	352	18	combined	combine	VERB
ajst-25954	352	19	with	with	ADP
ajst-25954	352	20	optimal	optimal	ADJ
ajst-25954	352	21	features	feature	NOUN
ajst-25954	352	22	strateg	strateg	NOUN
ajst-25954	352	23	-	-	PUNCT
ajst-25954	352	24	yon	yon	NOUN
ajst-25954	352	25	eeg	eeg	PROPN
ajst-25954	352	26	.	.	PUNCT
ajst-25954	352	27	frontiers	frontier	NOUN
ajst-25954	352	28	in	in	ADP
ajst-25954	352	29	neuroscience	neuroscience	NOUN
ajst-25954	352	30	,	,	PUNCT
ajst-25954	352	31	2023	2023	NUM
ajst-25954	352	32	.	.	PUNCT
ajst-25954	353	1	17	17	NUM
ajst-25954	353	2	.	.	PUNCT
ajst-25954	354	1	[	[	X
ajst-25954	354	2	33	33	NUM
ajst-25954	354	3	]	]	PUNCT
ajst-25954	354	4	m.	m.	NOUN
ajst-25954	354	5	ma	ma	PROPN
ajst-25954	354	6	et	et	PROPN
ajst-25954	354	7	al	al	PROPN
ajst-25954	354	8	.	.	PROPN
ajst-25954	354	9	,	,	PUNCT
ajst-25954	354	10	“	"	PUNCT
ajst-25954	354	11	early	early	ADJ
ajst-25954	354	12	prediction	prediction	NOUN
ajst-25954	354	13	of	of	ADP
ajst-25954	354	14	epileptic	epileptic	ADJ
ajst-25954	354	15	seizure	seizure	NOUN
ajst-25954	354	16	based	base	VERB
ajst-25954	354	17	on	on	ADP
ajst-25954	354	18	the	the	DET
ajst-25954	354	19	bnlsm	bnlsm	PROPN
ajst-25954	354	20	-	-	PUNCT
ajst-25954	354	21	casa	casa	PROPN
ajst-25954	354	22	model	model	NOUN
ajst-25954	354	23	,	,	PUNCT
ajst-25954	354	24	”	"	PUNCT
ajst-25954	354	25	ieee	ieee	NOUN
ajst-25954	354	26	access	access	NOUN
ajst-25954	354	27	,	,	PUNCT
ajst-25954	354	28	vol	vol	NOUN
ajst-25954	354	29	.	.	NOUN
ajst-25954	354	30	9	9	NUM
ajst-25954	354	31	,	,	PUNCT
ajst-25954	354	32	pp	pp	ADJ
ajst-25954	354	33	.	.	PUNCT
ajst-25954	355	1	79600	79600	NUM
ajst-25954	355	2	–	–	PUNCT
ajst-25954	355	3	79610	79610	NUM
ajst-25954	355	4	,	,	PUNCT
ajst-25954	355	5	2021	2021	NUM
ajst-25954	355	6	.	.	PUNCT
ajst-25954	356	1	[	[	X
ajst-25954	356	2	34	34	NUM
ajst-25954	356	3	]	]	X
ajst-25954	356	4	x.yang	x.yang	PROPN
ajst-25954	356	5	,	,	PUNCT
ajst-25954	356	6	j.	j.	PROPN
ajst-25954	356	7	zhao	zhao	PROPN
ajst-25954	356	8	,	,	PUNCT
ajst-25954	356	9	q.	q.	PROPN
ajst-25954	356	10	sun	sun	PROPN
ajst-25954	356	11	,	,	PUNCT
ajst-25954	356	12	j.	j.	PROPN
ajst-25954	356	13	lu	lu	PROPN
ajst-25954	356	14	,	,	PUNCT
ajst-25954	356	15	and	and	CCONJ
ajst-25954	356	16	x.	x.	PROPN
ajst-25954	356	17	ma	ma	PROPN
ajst-25954	356	18	,	,	PUNCT
ajst-25954	356	19	“	"	PUNCT
ajst-25954	356	20	an	an	DET
ajst-25954	356	21	effective	effective	ADJ
ajst-25954	356	22	dual	dual	ADJ
ajst-25954	356	23	self	self	NOUN
ajst-25954	356	24	-	-	PUNCT
ajst-25954	356	25	attention	attention	NOUN
ajst-25954	356	26	residual	residual	ADJ
ajst-25954	356	27	network	network	NOUN
ajst-25954	356	28	for	for	ADP
ajst-25954	356	29	seizure	seizure	NOUN
ajst-25954	356	30	prediction	prediction	NOUN
ajst-25954	356	31	,	,	PUNCT
ajst-25954	356	32	”	"	PUNCT
ajst-25954	356	33	ieee	ieee	NOUN
ajst-25954	356	34	trans	trans	PROPN
ajst-25954	356	35	.	.	PUNCT
ajst-25954	356	36	neural	neural	ADJ
ajst-25954	356	37	syst	syst	PROPN
ajst-25954	356	38	.	.	PUNCT
ajst-25954	356	39	rehabil	rehabil	PROPN
ajst-25954	356	40	.	.	PUNCT
ajst-25954	357	1	eng	eng	PROPN
ajst-25954	357	2	.	.	PROPN
ajst-25954	357	3	,	,	PUNCT
ajst-25954	357	4	vol	vol	NOUN
ajst-25954	357	5	.	.	PROPN
ajst-25954	357	6	29	29	NUM
ajst-25954	357	7	,	,	PUNCT
ajst-25954	357	8	pp	pp	ADJ
ajst-25954	357	9	.	.	PUNCT
ajst-25954	358	1	1604–1613	1604–1613	NUM
ajst-25954	358	2	,	,	PUNCT
ajst-25954	358	3	2021	2021	NUM
ajst-25954	358	4	.	.	PUNCT
ajst-25954	359	1	[	[	X
ajst-25954	359	2	35	35	NUM
ajst-25954	359	3	]	]	X
ajst-25954	359	4	s.	s.	PROPN
ajst-25954	359	5	m.	m.	PROPN
ajst-25954	359	6	usman	usman	PROPN
ajst-25954	359	7	,	,	PUNCT
ajst-25954	359	8	s.	s.	PROPN
ajst-25954	359	9	khalid	khalid	PROPN
ajst-25954	359	10	,	,	PUNCT
ajst-25954	359	11	and	and	CCONJ
ajst-25954	359	12	m.	m.	PROPN
ajst-25954	359	13	h.	h.	PROPN
ajst-25954	359	14	aslam	aslam	PROPN
ajst-25954	359	15	,	,	PUNCT
ajst-25954	359	16	“	"	PUNCT
ajst-25954	359	17	epileptic	epileptic	ADJ
ajst-25954	359	18	seizures	seizure	NOUN
ajst-25954	359	19	prediction	prediction	NOUN
ajst-25954	359	20	using	use	VERB
ajst-25954	359	21	deep	deep	ADJ
ajst-25954	359	22	learning	learning	NOUN
ajst-25954	359	23	techniques	technique	NOUN
ajst-25954	359	24	,	,	PUNCT
ajst-25954	359	25	”	"	PUNCT
ajst-25954	359	26	ieee	ieee	NOUN
ajst-25954	359	27	access	access	NOUN
ajst-25954	359	28	,	,	PUNCT
ajst-25954	359	29	vol	vol	NOUN
ajst-25954	359	30	.	.	PROPN
ajst-25954	359	31	8	8	NUM
ajst-25954	359	32	,	,	PUNCT
ajst-25954	359	33	pp	pp	ADJ
ajst-25954	359	34	.	.	PUNCT
ajst-25954	360	1	39998–40007,2020	39998–40007,2020	NUM
ajst-25954	360	2	.	.	PUNCT
ajst-25954	361	1	[	[	X
ajst-25954	361	2	36	36	NUM
ajst-25954	361	3	]	]	PUNCT
ajst-25954	361	4	turk	turk	NOUN
ajst-25954	361	5	,	,	PUNCT
ajst-25954	361	6	o.	o.	PROPN
ajst-25954	361	7	and	and	CCONJ
ajst-25954	361	8	m.s	m.s	PROPN
ajst-25954	361	9	.	.	PROPN
ajst-25954	361	10	ozerdem	ozerdem	PROPN
ajst-25954	361	11	,	,	PUNCT
ajst-25954	361	12	epilepsy	epilepsy	ADJ
ajst-25954	361	13	detection	detection	NOUN
ajst-25954	361	14	by	by	ADP
ajst-25954	361	15	using	use	VERB
ajst-25954	361	16	scalogram	scalogram	NOUN
ajst-25954	361	17	based	base	VERB
ajst-25954	361	18	convolutional	convolutional	ADJ
ajst-25954	361	19	neural	neural	ADJ
ajst-25954	361	20	network	network	NOUN
ajst-25954	361	21	from	from	ADP
ajst-25954	361	22	eeg	eeg	NOUN
ajst-25954	361	23	signals	signal	NOUN
ajst-25954	361	24	.	.	PUNCT
ajst-25954	362	1	brain	brain	NOUN
ajst-25954	362	2	sciences	sciences	PROPN
ajst-25954	362	3	,	,	PUNCT
ajst-25954	362	4	2019	2019	NUM
ajst-25954	362	5	.	.	PUNCT
ajst-25954	363	1	9(5	9(5	NUM
ajst-25954	363	2	)	)	PUNCT
ajst-25954	363	3	.	.	PUNCT
ajst-25954	364	1	[	[	X
ajst-25954	364	2	37	37	NUM
ajst-25954	364	3	]	]	PUNCT
ajst-25954	364	4	m.	m.	NOUN
ajst-25954	364	5	chakraborty	chakraborty	PROPN
ajst-25954	364	6	,	,	PUNCT
ajst-25954	364	7	d.	d.	PROPN
ajst-25954	364	8	mitra	mitra	PROPN
ajst-25954	364	9	,	,	PUNCT
ajst-25954	364	10	a	a	DET
ajst-25954	364	11	computationally	computationally	ADV
ajst-25954	364	12	efficient	efficient	ADJ
ajst-25954	364	13	automated	automate	VERB
ajst-25954	364	14	seizure	seizure	NOUN
ajst-25954	364	15	detection	detection	NOUN
ajst-25954	364	16	method	method	NOUN
ajst-25954	364	17	based	base	VERB
ajst-25954	364	18	on	on	ADP
ajst-25954	364	19	the	the	DET
ajst-25954	364	20	novel	novel	ADJ
ajst-25954	364	21	idea	idea	NOUN
ajst-25954	364	22	of	of	ADP
ajst-25954	364	23	multiscale	multiscale	ADJ
ajst-25954	364	24	spectral	spectral	ADJ
ajst-25954	364	25	features	feature	NOUN
ajst-25954	364	26	,	,	PUNCT
ajst-25954	364	27	biomed.signal	biomed.signal	ADJ
ajst-25954	364	28	process	process	NOUN
ajst-25954	364	29	.	.	PUNCT
ajst-25954	365	1	control	control	NOUN
ajst-25954	365	2	.	.	PUNCT
ajst-25954	366	1	70	70	NUM
ajst-25954	366	2	(	(	PUNCT
ajst-25954	366	3	2021	2021	NUM
ajst-25954	366	4	)	)	PUNCT
ajst-25954	366	5	102990	102990	NUM
ajst-25954	366	6	sep	sep	NOUN
ajst-25954	366	7	1	1	NUM
ajst-25954	366	8	.	.	PUNCT
ajst-25954	367	1	[	[	X
ajst-25954	367	2	38	38	NUM
ajst-25954	367	3	]	]	PUNCT
ajst-25954	367	4	w.	w.	PROPN
ajst-25954	367	5	zhao	zhao	PROPN
ajst-25954	367	6	,	,	PUNCT
ajst-25954	367	7	w.	w.	PROPN
ajst-25954	367	8	zhao	zhao	PROPN
ajst-25954	367	9	,	,	PUNCT
ajst-25954	367	10	w.	w.	PROPN
ajst-25954	367	11	wang	wang	PROPN
ajst-25954	367	12	,	,	PUNCT
ajst-25954	367	13	x.	x.	PROPN
ajst-25954	367	14	jiang	jiang	PROPN
ajst-25954	367	15	,	,	PUNCT
ajst-25954	367	16	x.	x.	PROPN
ajst-25954	367	17	zhang	zhang	PROPN
ajst-25954	367	18	,	,	PUNCT
ajst-25954	367	19	y.	y.	PROPN
ajst-25954	367	20	peng	peng	PROPN
ajst-25954	367	21	,	,	PUNCT
ajst-25954	367	22	b.	b.	PROPN
ajst-25954	367	23	zhang	zhang	PROPN
ajst-25954	367	24	,	,	PUNCT
ajst-25954	367	25	g.	g.	PROPN
ajst-25954	367	26	zhang	zhang	PROPN
ajst-25954	367	27	,	,	PUNCT
ajst-25954	367	28	a	a	DET
ajst-25954	367	29	novel	novel	ADJ
ajst-25954	367	30	deep	deep	ADJ
ajst-25954	367	31	neural	neural	ADJ
ajst-25954	367	32	network	network	NOUN
ajst-25954	367	33	for	for	ADP
ajst-25954	367	34	robust	robust	ADJ
ajst-25954	367	35	detection	detection	NOUN
ajst-25954	367	36	of	of	ADP
ajst-25954	367	37	seizures	seizure	NOUN
ajst-25954	367	38	using	use	VERB
ajst-25954	367	39	eeg	eeg	NOUN
ajst-25954	367	40	signals	signal	NOUN
ajst-25954	367	41	,	,	PUNCT
ajst-25954	367	42	comput	comput	NOUN
ajst-25954	367	43	.	.	PUNCT
ajst-25954	368	1	math	math	NOUN
ajst-25954	368	2	.	.	PUNCT
ajst-25954	369	1	methods	method	NOUN
ajst-25954	369	2	med	me	VERB
ajst-25954	369	3	.	.	PUNCT
ajst-25954	370	1	2020	2020	NUM
ajst-25954	370	2	(	(	PUNCT
ajst-25954	370	3	2020	2020	NUM
ajst-25954	370	4	)	)	PUNCT
ajst-25954	370	5	2020	2020	NUM
ajst-25954	370	6	apr	apr	VERB
ajst-25954	370	7	7	7	NUM
ajst-25954	370	8	.	.	PUNCT
ajst-25954	371	1	[	[	X
ajst-25954	371	2	39	39	NUM
ajst-25954	371	3	]	]	PUNCT
ajst-25954	371	4	rashed	rashe	VERB
ajst-25954	371	5	-	-	PUNCT
ajst-25954	371	6	al	al	PROPN
ajst-25954	371	7	-	-	PUNCT
ajst-25954	371	8	mahfuz	mahfuz	NOUN
ajst-25954	371	9	,	,	PUNCT
ajst-25954	371	10	m.	m.	NOUN
ajst-25954	371	11	,	,	PUNCT
ajst-25954	371	12	et	et	PROPN
ajst-25954	371	13	al	al	PROPN
ajst-25954	371	14	.	.	PROPN
ajst-25954	371	15	,	,	PUNCT
ajst-25954	371	16	a	a	DET
ajst-25954	371	17	deep	deep	ADJ
ajst-25954	371	18	convolution	convolution	NOUN
ajst-25954	371	19	-	-	PUNCT
ajst-25954	371	20	al	al	PROPN
ajst-25954	371	21	neural	neural	ADJ
ajst-25954	371	22	network	network	NOUN
ajst-25954	371	23	method	method	NOUN
ajst-25954	371	24	to	to	PART
ajst-25954	371	25	detect	detect	VERB
ajst-25954	371	26	seizures	seizure	NOUN
ajst-25954	371	27	and	and	CCONJ
ajst-25954	371	28	chara	chara	NOUN
ajst-25954	371	29	-	-	PUNCT
ajst-25954	371	30	cteristic	cteristic	ADJ
ajst-25954	371	31	frequencies	frequency	NOUN
ajst-25954	371	32	using	use	VERB
ajst-25954	371	33	epileptic	epileptic	ADJ
ajst-25954	371	34	electroencephalogra	electroencephalogra	ADJ
ajst-25954	371	35	-	-	PUNCT
ajst-25954	371	36	m	m	PROPN
ajst-25954	371	37	(	(	PUNCT
ajst-25954	371	38	eeg	eeg	PROPN
ajst-25954	371	39	)	)	PUNCT
ajst-25954	371	40	data	datum	NOUN
ajst-25954	371	41	.	.	PUNCT
ajst-25954	372	1	ieee	ieee	PROPN
ajst-25954	372	2	journal	journal	PROPN
ajst-25954	372	3	of	of	ADP
ajst-25954	372	4	translational	translational	ADJ
ajst-25954	372	5	engineering	engineering	NOUN
ajst-25954	372	6	in	in	ADP
ajst-25954	372	7	health	health	NOUN
ajst-25954	372	8	and	and	CCONJ
ajst-25954	372	9	medicine	medicine	NOUN
ajst-25954	372	10	,	,	PUNCT
ajst-25954	372	11	2021.9	2021.9	NUM
ajst-25954	372	12	.	.	PUNCT