id	sid	tid	token	lemma	pos
fcis-13848	1	1	frontiers	frontier	NOUN
fcis-13848	1	2	in	in	ADP
fcis-13848	1	3	computing	computing	NOUN
fcis-13848	1	4	and	and	CCONJ
fcis-13848	1	5	intelligent	intelligent	ADJ
fcis-13848	1	6	systems	system	NOUN
fcis-13848	1	7	issn	issn	VERB
fcis-13848	1	8	:	:	PUNCT
fcis-13848	1	9	2832	2832	NUM
fcis-13848	1	10	-	-	SYM
fcis-13848	1	11	6024	6024	NUM
fcis-13848	1	12	|	|	NOUN
fcis-13848	1	13	vol	vol	NOUN
fcis-13848	1	14	.	.	PROPN
fcis-13848	2	1	5	5	NUM
fcis-13848	2	2	,	,	PUNCT
fcis-13848	2	3	no	no	INTJ
fcis-13848	2	4	.	.	NOUN
fcis-13848	2	5	3	3	NUM
fcis-13848	2	6	,	,	PUNCT
fcis-13848	2	7	2023	2023	NUM
fcis-13848	2	8	43	43	NUM
fcis-13848	2	9	research	research	NOUN
fcis-13848	2	10	on	on	ADP
fcis-13848	2	11	arrhythmia	arrhythmia	NOUN
fcis-13848	2	12	classification	classification	NOUN
fcis-13848	2	13	and	and	CCONJ
fcis-13848	2	14	risk	risk	NOUN
fcis-13848	2	15	degree	degree	NOUN
fcis-13848	2	16	prediction	prediction	NOUN
fcis-13848	2	17	based	base	VERB
fcis-13848	2	18	on	on	ADP
fcis-13848	2	19	deep	deep	ADJ
fcis-13848	2	20	neural	neural	ADJ
fcis-13848	2	21	network	network	NOUN
fcis-13848	2	22	and	and	CCONJ
fcis-13848	2	23	convolutional	convolutional	ADJ
fcis-13848	2	24	neural	neural	ADJ
fcis-13848	2	25	network	network	NOUN
fcis-13848	2	26	songling	songle	VERB
fcis-13848	2	27	huang	huang	PROPN
fcis-13848	2	28	,	,	PUNCT
fcis-13848	2	29	zhenji	zhenji	X
fcis-13848	2	30	wen	wen	PROPN
fcis-13848	2	31	,	,	PUNCT
fcis-13848	2	32	hanling	hanle	VERB
fcis-13848	2	33	li	li	PROPN
fcis-13848	2	34	college	college	PROPN
fcis-13848	2	35	of	of	ADP
fcis-13848	2	36	big	big	ADJ
fcis-13848	2	37	data	data	PROPN
fcis-13848	2	38	,	,	PUNCT
fcis-13848	2	39	yunnan	yunnan	PROPN
fcis-13848	2	40	agricultural	agricultural	PROPN
fcis-13848	2	41	university	university	PROPN
fcis-13848	2	42	,	,	PUNCT
fcis-13848	2	43	yunnan	yunnan	NOUN
fcis-13848	2	44	,	,	PUNCT
fcis-13848	2	45	650201	650201	NUM
fcis-13848	2	46	,	,	PUNCT
fcis-13848	2	47	china	china	PROPN
fcis-13848	2	48	abstract	abstract	NOUN
fcis-13848	2	49	:	:	PUNCT
fcis-13848	2	50	in	in	ADP
fcis-13848	2	51	this	this	DET
fcis-13848	2	52	study	study	NOUN
fcis-13848	2	53	,	,	PUNCT
fcis-13848	2	54	a	a	DET
fcis-13848	2	55	method	method	NOUN
fcis-13848	2	56	of	of	ADP
fcis-13848	2	57	arrhythmia	arrhythmia	NOUN
fcis-13848	2	58	classification	classification	NOUN
fcis-13848	2	59	and	and	CCONJ
fcis-13848	2	60	risk	risk	NOUN
fcis-13848	2	61	prediction	prediction	NOUN
fcis-13848	2	62	based	base	VERB
fcis-13848	2	63	on	on	ADP
fcis-13848	2	64	deep	deep	ADJ
fcis-13848	2	65	neural	neural	ADJ
fcis-13848	2	66	network	network	NOUN
fcis-13848	2	67	and	and	CCONJ
fcis-13848	2	68	convolutional	convolutional	ADJ
fcis-13848	2	69	neural	neural	ADJ
fcis-13848	2	70	network	network	NOUN
fcis-13848	2	71	(	(	PUNCT
fcis-13848	2	72	cnn	cnn	PROPN
fcis-13848	2	73	)	)	PUNCT
fcis-13848	2	74	is	be	AUX
fcis-13848	2	75	proposed	propose	VERB
fcis-13848	2	76	for	for	ADP
fcis-13848	2	77	ecg	ecg	PROPN
fcis-13848	2	78	data	data	PROPN
fcis-13848	2	79	.	.	PUNCT
fcis-13848	3	1	electrocardiogram	electrocardiogram	NOUN
fcis-13848	3	2	data	datum	NOUN
fcis-13848	3	3	record	record	VERB
fcis-13848	3	4	the	the	DET
fcis-13848	3	5	electrophysiological	electrophysiological	ADJ
fcis-13848	3	6	activity	activity	NOUN
fcis-13848	3	7	of	of	ADP
fcis-13848	3	8	the	the	DET
fcis-13848	3	9	heart	heart	NOUN
fcis-13848	3	10	,	,	PUNCT
fcis-13848	3	11	including	include	VERB
fcis-13848	3	12	normal	normal	ADJ
fcis-13848	3	13	heart	heart	NOUN
fcis-13848	3	14	beats	beat	NOUN
fcis-13848	3	15	and	and	CCONJ
fcis-13848	3	16	various	various	ADJ
fcis-13848	3	17	arrhythmias	arrhythmia	NOUN
fcis-13848	3	18	.	.	PUNCT
fcis-13848	4	1	in	in	ADP
fcis-13848	4	2	order	order	NOUN
fcis-13848	4	3	to	to	PART
fcis-13848	4	4	monitor	monitor	VERB
fcis-13848	4	5	and	and	CCONJ
fcis-13848	4	6	identify	identify	VERB
fcis-13848	4	7	arrhythmia	arrhythmia	NOUN
fcis-13848	4	8	in	in	ADP
fcis-13848	4	9	real	real	ADJ
fcis-13848	4	10	time	time	NOUN
fcis-13848	4	11	and	and	CCONJ
fcis-13848	4	12	accurately	accurately	ADV
fcis-13848	4	13	,	,	PUNCT
fcis-13848	4	14	this	this	DET
fcis-13848	4	15	study	study	NOUN
fcis-13848	4	16	used	use	VERB
fcis-13848	4	17	cnn	cnn	PROPN
fcis-13848	4	18	model	model	NOUN
fcis-13848	4	19	for	for	ADP
fcis-13848	4	20	data	datum	NOUN
fcis-13848	4	21	analysis	analysis	NOUN
fcis-13848	4	22	.	.	PUNCT
fcis-13848	5	1	the	the	DET
fcis-13848	5	2	characteristics	characteristic	NOUN
fcis-13848	5	3	of	of	ADP
fcis-13848	5	4	cnn	cnn	PROPN
fcis-13848	5	5	,	,	PUNCT
fcis-13848	5	6	such	such	ADJ
fcis-13848	5	7	as	as	ADP
fcis-13848	5	8	local	local	ADJ
fcis-13848	5	9	perception	perception	NOUN
fcis-13848	5	10	,	,	PUNCT
fcis-13848	5	11	parameter	parameter	NOUN
fcis-13848	5	12	sharing	sharing	NOUN
fcis-13848	5	13	and	and	CCONJ
fcis-13848	5	14	multi	multi	ADJ
fcis-13848	5	15	-	-	ADJ
fcis-13848	5	16	level	level	ADJ
fcis-13848	5	17	feature	feature	NOUN
fcis-13848	5	18	extraction	extraction	NOUN
fcis-13848	5	19	,	,	PUNCT
fcis-13848	5	20	make	make	VERB
fcis-13848	5	21	it	it	PRON
fcis-13848	5	22	perform	perform	VERB
fcis-13848	5	23	well	well	ADV
fcis-13848	5	24	in	in	ADP
fcis-13848	5	25	ecg	ecg	PROPN
fcis-13848	5	26	data	datum	NOUN
fcis-13848	5	27	analysis	analysis	NOUN
fcis-13848	5	28	.	.	PUNCT
fcis-13848	6	1	the	the	DET
fcis-13848	6	2	data	data	NOUN
fcis-13848	6	3	comes	come	VERB
fcis-13848	6	4	from	from	ADP
fcis-13848	6	5	the	the	DET
fcis-13848	6	6	'	'	PUNCT
fcis-13848	6	7	certification	certification	NOUN
fcis-13848	6	8	cup	cup	NOUN
fcis-13848	6	9	'	'	PART
fcis-13848	6	10	mathematics	mathematics	PROPN
fcis-13848	6	11	china	china	PROPN
fcis-13848	6	12	mathematical	mathematical	ADJ
fcis-13848	6	13	modeling	modeling	NOUN
fcis-13848	6	14	network	network	NOUN
fcis-13848	6	15	challenge	challenge	NOUN
fcis-13848	6	16	in	in	ADP
fcis-13848	6	17	2023	2023	NUM
fcis-13848	6	18	and	and	CCONJ
fcis-13848	6	19	is	be	AUX
fcis-13848	6	20	preprocessed	preprocesse	VERB
fcis-13848	6	21	to	to	PART
fcis-13848	6	22	meet	meet	VERB
fcis-13848	6	23	the	the	DET
fcis-13848	6	24	needs	need	NOUN
fcis-13848	6	25	of	of	ADP
fcis-13848	6	26	the	the	DET
fcis-13848	6	27	model	model	NOUN
fcis-13848	6	28	.	.	PUNCT
fcis-13848	7	1	in	in	ADP
fcis-13848	7	2	the	the	DET
fcis-13848	7	3	process	process	NOUN
fcis-13848	7	4	of	of	ADP
fcis-13848	7	5	establishing	establish	VERB
fcis-13848	7	6	and	and	CCONJ
fcis-13848	7	7	solving	solve	VERB
fcis-13848	7	8	the	the	DET
fcis-13848	7	9	model	model	NOUN
fcis-13848	7	10	,	,	PUNCT
fcis-13848	7	11	the	the	DET
fcis-13848	7	12	cross	cross	ADJ
fcis-13848	7	13	-	-	ADJ
fcis-13848	7	14	entropy	entropy	ADJ
fcis-13848	7	15	loss	loss	NOUN
fcis-13848	7	16	function	function	NOUN
fcis-13848	7	17	is	be	AUX
fcis-13848	7	18	used	use	VERB
fcis-13848	7	19	to	to	PART
fcis-13848	7	20	optimize	optimize	VERB
fcis-13848	7	21	,	,	PUNCT
fcis-13848	7	22	and	and	CCONJ
fcis-13848	7	23	the	the	DET
fcis-13848	7	24	effectiveness	effectiveness	NOUN
fcis-13848	7	25	and	and	CCONJ
fcis-13848	7	26	robustness	robustness	NOUN
fcis-13848	7	27	of	of	ADP
fcis-13848	7	28	the	the	DET
fcis-13848	7	29	model	model	NOUN
fcis-13848	7	30	are	be	AUX
fcis-13848	7	31	verified	verify	VERB
fcis-13848	7	32	by	by	ADP
fcis-13848	7	33	various	various	ADJ
fcis-13848	7	34	evaluation	evaluation	NOUN
fcis-13848	7	35	methods	method	NOUN
fcis-13848	7	36	.	.	PUNCT
fcis-13848	8	1	the	the	DET
fcis-13848	8	2	results	result	NOUN
fcis-13848	8	3	show	show	VERB
fcis-13848	8	4	that	that	SCONJ
fcis-13848	8	5	the	the	DET
fcis-13848	8	6	model	model	NOUN
fcis-13848	8	7	can	can	AUX
fcis-13848	8	8	accurately	accurately	ADV
fcis-13848	8	9	classify	classify	VERB
fcis-13848	8	10	and	and	CCONJ
fcis-13848	8	11	predict	predict	VERB
fcis-13848	8	12	the	the	DET
fcis-13848	8	13	risk	risk	NOUN
fcis-13848	8	14	of	of	ADP
fcis-13848	8	15	arrhythmia	arrhythmia	NOUN
fcis-13848	8	16	,	,	PUNCT
fcis-13848	8	17	providing	provide	VERB
fcis-13848	8	18	a	a	DET
fcis-13848	8	19	powerful	powerful	ADJ
fcis-13848	8	20	diagnostic	diagnostic	ADJ
fcis-13848	8	21	tool	tool	NOUN
fcis-13848	8	22	for	for	ADP
fcis-13848	8	23	doctors	doctor	NOUN
fcis-13848	8	24	and	and	CCONJ
fcis-13848	8	25	a	a	DET
fcis-13848	8	26	valuable	valuable	ADJ
fcis-13848	8	27	reference	reference	NOUN
fcis-13848	8	28	for	for	ADP
fcis-13848	8	29	future	future	ADJ
fcis-13848	8	30	arrhythmia	arrhythmia	NOUN
fcis-13848	8	31	research	research	NOUN
fcis-13848	8	32	.	.	PUNCT
fcis-13848	9	1	keywords	keyword	NOUN
fcis-13848	9	2	:	:	PUNCT
fcis-13848	9	3	arrhythmia	arrhythmia	NOUN
fcis-13848	9	4	;	;	PUNCT
fcis-13848	9	5	degree	degree	NOUN
fcis-13848	9	6	of	of	ADP
fcis-13848	9	7	danger	danger	NOUN
fcis-13848	9	8	;	;	PUNCT
fcis-13848	9	9	prediction	prediction	NOUN
fcis-13848	9	10	;	;	PUNCT
fcis-13848	9	11	cnn	cnn	PROPN
fcis-13848	9	12	;	;	PUNCT
fcis-13848	9	13	cross	cross	NOUN
fcis-13848	9	14	entropy	entropy	PROPN
fcis-13848	9	15	loss	loss	NOUN
fcis-13848	9	16	function	function	NOUN
fcis-13848	9	17	.	.	PUNCT
fcis-13848	10	1	1	1	X
fcis-13848	10	2	.	.	X
fcis-13848	10	3	introduction	introduction	NOUN
fcis-13848	10	4	every	every	DET
fcis-13848	10	5	pulse	pulse	NOUN
fcis-13848	10	6	of	of	ADP
fcis-13848	10	7	the	the	DET
fcis-13848	10	8	heart	heart	NOUN
fcis-13848	10	9	is	be	AUX
fcis-13848	10	10	accompanied	accompany	VERB
fcis-13848	10	11	by	by	ADP
fcis-13848	10	12	electrophysiological	electrophysiological	ADJ
fcis-13848	10	13	activity	activity	NOUN
fcis-13848	10	14	.	.	PUNCT
fcis-13848	11	1	this	this	DET
fcis-13848	11	2	electrical	electrical	ADJ
fcis-13848	11	3	signal	signal	NOUN
fcis-13848	11	4	can	can	AUX
fcis-13848	11	5	be	be	AUX
fcis-13848	11	6	transmitted	transmit	VERB
fcis-13848	11	7	to	to	ADP
fcis-13848	11	8	the	the	DET
fcis-13848	11	9	skin	skin	NOUN
fcis-13848	11	10	of	of	ADP
fcis-13848	11	11	the	the	DET
fcis-13848	11	12	body	body	NOUN
fcis-13848	11	13	surface	surface	NOUN
fcis-13848	11	14	and	and	CCONJ
fcis-13848	11	15	recorded	record	VERB
fcis-13848	11	16	by	by	ADP
fcis-13848	11	17	the	the	DET
fcis-13848	11	18	electrocardiogram	electrocardiogram	NOUN
fcis-13848	11	19	machine	machine	NOUN
fcis-13848	11	20	.	.	PUNCT
fcis-13848	12	1	the	the	DET
fcis-13848	12	2	ecg	ecg	PROPN
fcis-13848	12	3	data	data	PROPN
fcis-13848	12	4	contains	contain	VERB
fcis-13848	12	5	many	many	ADJ
fcis-13848	12	6	representative	representative	ADJ
fcis-13848	12	7	fragments	fragment	NOUN
fcis-13848	12	8	,	,	PUNCT
fcis-13848	12	9	including	include	VERB
fcis-13848	12	10	normal	normal	ADJ
fcis-13848	12	11	heartbeats	heartbeat	NOUN
fcis-13848	12	12	and	and	CCONJ
fcis-13848	12	13	various	various	ADJ
fcis-13848	12	14	arrhythmias	arrhythmia	NOUN
fcis-13848	12	15	.	.	PUNCT
fcis-13848	13	1	in	in	ADP
fcis-13848	13	2	order	order	NOUN
fcis-13848	13	3	to	to	PART
fcis-13848	13	4	realize	realize	VERB
fcis-13848	13	5	the	the	DET
fcis-13848	13	6	real	real	ADJ
fcis-13848	13	7	-	-	PUNCT
fcis-13848	13	8	time	time	NOUN
fcis-13848	13	9	alarm	alarm	NOUN
fcis-13848	13	10	of	of	ADP
fcis-13848	13	11	ecg	ecg	PROPN
fcis-13848	13	12	monitoring	monitoring	NOUN
fcis-13848	13	13	,	,	PUNCT
fcis-13848	13	14	it	it	PRON
fcis-13848	13	15	is	be	AUX
fcis-13848	13	16	necessary	necessary	ADJ
fcis-13848	13	17	to	to	PART
fcis-13848	13	18	make	make	VERB
fcis-13848	13	19	correct	correct	ADJ
fcis-13848	13	20	judgment	judgment	NOUN
fcis-13848	13	21	on	on	ADP
fcis-13848	13	22	arrhythmia	arrhythmia	NOUN
fcis-13848	13	23	in	in	ADP
fcis-13848	13	24	a	a	DET
fcis-13848	13	25	short	short	ADJ
fcis-13848	13	26	time	time	NOUN
fcis-13848	13	27	.	.	PUNCT
fcis-13848	14	1	the	the	DET
fcis-13848	14	2	length	length	NOUN
fcis-13848	14	3	of	of	ADP
fcis-13848	14	4	each	each	DET
fcis-13848	14	5	ecg	ecg	PROPN
fcis-13848	14	6	segment	segment	NOUN
fcis-13848	14	7	was	be	AUX
fcis-13848	14	8	2	2	NUM
fcis-13848	14	9	seconds	second	NOUN
fcis-13848	14	10	,	,	PUNCT
fcis-13848	14	11	and	and	CCONJ
fcis-13848	14	12	the	the	DET
fcis-13848	14	13	power	power	NOUN
fcis-13848	14	14	spectral	spectral	ADJ
fcis-13848	14	15	density	density	NOUN
fcis-13848	14	16	of	of	ADP
fcis-13848	14	17	the	the	DET
fcis-13848	14	18	ecg	ecg	PROPN
fcis-13848	14	19	wave	wave	NOUN
fcis-13848	14	20	was	be	AUX
fcis-13848	14	21	recorded	record	VERB
fcis-13848	14	22	from	from	ADP
fcis-13848	14	23	0	0	NUM
fcis-13848	14	24	hz	hz	VERB
fcis-13848	14	25	to	to	ADP
fcis-13848	14	26	180	180	NUM
fcis-13848	14	27	hz	hz	NOUN
fcis-13848	14	28	,	,	PUNCT
fcis-13848	14	29	with	with	ADP
fcis-13848	14	30	a	a	DET
fcis-13848	14	31	frequency	frequency	NOUN
fcis-13848	14	32	interval	interval	NOUN
fcis-13848	14	33	of	of	ADP
fcis-13848	14	34	0.5	0.5	NUM
fcis-13848	14	35	hz	hz	NOUN
fcis-13848	14	36	.	.	PUNCT
fcis-13848	15	1	these	these	DET
fcis-13848	15	2	data	datum	NOUN
fcis-13848	15	3	can	can	AUX
fcis-13848	15	4	be	be	AUX
fcis-13848	15	5	used	use	VERB
fcis-13848	15	6	to	to	PART
fcis-13848	15	7	train	train	VERB
fcis-13848	15	8	and	and	CCONJ
fcis-13848	15	9	optimize	optimize	VERB
fcis-13848	15	10	deep	deep	ADJ
fcis-13848	15	11	neural	neural	ADJ
fcis-13848	15	12	network	network	NOUN
fcis-13848	15	13	models	model	NOUN
fcis-13848	15	14	to	to	PART
fcis-13848	15	15	classify	classify	VERB
fcis-13848	15	16	and	and	CCONJ
fcis-13848	15	17	predict	predict	VERB
fcis-13848	15	18	the	the	DET
fcis-13848	15	19	risk	risk	NOUN
fcis-13848	15	20	of	of	ADP
fcis-13848	15	21	arrhythmia	arrhythmia	NOUN
fcis-13848	15	22	and	and	CCONJ
fcis-13848	15	23	help	help	VERB
fcis-13848	15	24	doctors	doctor	NOUN
fcis-13848	15	25	better	well	ADV
fcis-13848	15	26	diagnose	diagnose	VERB
fcis-13848	15	27	and	and	CCONJ
fcis-13848	15	28	treat	treat	VERB
fcis-13848	15	29	heart	heart	NOUN
fcis-13848	15	30	disease	disease	NOUN
fcis-13848	16	1	[	[	X
fcis-13848	16	2	1	1	X
fcis-13848	16	3	]	]	X
fcis-13848	16	4	[	[	X
fcis-13848	16	5	2	2	NUM
fcis-13848	16	6	]	]	PUNCT
fcis-13848	16	7	.	.	PUNCT
fcis-13848	17	1	arrhythmia	arrhythmia	NOUN
fcis-13848	17	2	is	be	AUX
fcis-13848	17	3	a	a	DET
fcis-13848	17	4	common	common	ADJ
fcis-13848	17	5	heart	heart	NOUN
fcis-13848	17	6	disease	disease	NOUN
fcis-13848	17	7	that	that	PRON
fcis-13848	17	8	can	can	AUX
fcis-13848	17	9	lead	lead	VERB
fcis-13848	17	10	to	to	ADP
fcis-13848	17	11	lifethreatening	lifethreatene	VERB
fcis-13848	17	12	consequences	consequence	NOUN
fcis-13848	17	13	,	,	PUNCT
fcis-13848	17	14	such	such	ADJ
fcis-13848	17	15	as	as	ADP
fcis-13848	17	16	sudden	sudden	ADJ
fcis-13848	17	17	cardiac	cardiac	ADJ
fcis-13848	17	18	death	death	NOUN
fcis-13848	17	19	.	.	PUNCT
fcis-13848	18	1	accurate	accurate	ADJ
fcis-13848	18	2	real	real	ADJ
fcis-13848	18	3	-	-	PUNCT
fcis-13848	18	4	time	time	NOUN
fcis-13848	18	5	monitoring	monitoring	NOUN
fcis-13848	18	6	of	of	ADP
fcis-13848	18	7	ecg	ecg	PROPN
fcis-13848	18	8	signals	signal	NOUN
fcis-13848	18	9	and	and	CCONJ
fcis-13848	18	10	identification	identification	NOUN
fcis-13848	18	11	of	of	ADP
fcis-13848	18	12	arrhythmia	arrhythmia	NOUN
fcis-13848	18	13	types	type	NOUN
fcis-13848	18	14	and	and	CCONJ
fcis-13848	18	15	risk	risk	NOUN
fcis-13848	18	16	levels	level	NOUN
fcis-13848	18	17	are	be	AUX
fcis-13848	18	18	of	of	ADP
fcis-13848	18	19	great	great	ADJ
fcis-13848	18	20	significance	significance	NOUN
fcis-13848	18	21	for	for	ADP
fcis-13848	18	22	the	the	DET
fcis-13848	18	23	prevention	prevention	NOUN
fcis-13848	18	24	and	and	CCONJ
fcis-13848	18	25	treatment	treatment	NOUN
fcis-13848	18	26	of	of	ADP
fcis-13848	18	27	arrhythmia	arrhythmia	NOUN
fcis-13848	18	28	diseases	disease	NOUN
fcis-13848	18	29	.	.	PUNCT
fcis-13848	19	1	through	through	ADP
fcis-13848	19	2	the	the	DET
fcis-13848	19	3	analysis	analysis	NOUN
fcis-13848	19	4	and	and	CCONJ
fcis-13848	19	5	processing	processing	NOUN
fcis-13848	19	6	of	of	ADP
fcis-13848	19	7	ecg	ecg	PROPN
fcis-13848	19	8	signals	signal	NOUN
fcis-13848	19	9	,	,	PUNCT
fcis-13848	19	10	a	a	DET
fcis-13848	19	11	deep	deep	ADJ
fcis-13848	19	12	neural	neural	ADJ
fcis-13848	19	13	network	network	NOUN
fcis-13848	19	14	model	model	NOUN
fcis-13848	19	15	that	that	PRON
fcis-13848	19	16	can	can	AUX
fcis-13848	19	17	classify	classify	VERB
fcis-13848	19	18	and	and	CCONJ
fcis-13848	19	19	predict	predict	VERB
fcis-13848	19	20	the	the	DET
fcis-13848	19	21	risk	risk	NOUN
fcis-13848	19	22	of	of	ADP
fcis-13848	19	23	arrhythmia	arrhythmia	NOUN
fcis-13848	19	24	can	can	AUX
fcis-13848	19	25	be	be	AUX
fcis-13848	19	26	established	establish	VERB
fcis-13848	19	27	,	,	PUNCT
fcis-13848	19	28	which	which	PRON
fcis-13848	19	29	can	can	AUX
fcis-13848	19	30	help	help	VERB
fcis-13848	19	31	doctors	doctor	NOUN
fcis-13848	19	32	diagnose	diagnose	VERB
fcis-13848	19	33	the	the	DET
fcis-13848	19	34	patient	patient	NOUN
fcis-13848	19	35	's	's	PART
fcis-13848	19	36	condition	condition	NOUN
fcis-13848	19	37	faster	fast	ADV
fcis-13848	19	38	and	and	CCONJ
fcis-13848	19	39	more	more	ADV
fcis-13848	19	40	accurately	accurately	ADV
fcis-13848	19	41	,	,	PUNCT
fcis-13848	19	42	take	take	VERB
fcis-13848	19	43	timely	timely	ADJ
fcis-13848	19	44	treatment	treatment	NOUN
fcis-13848	19	45	measures	measure	NOUN
fcis-13848	19	46	,	,	PUNCT
fcis-13848	19	47	and	and	CCONJ
fcis-13848	19	48	improve	improve	VERB
fcis-13848	19	49	the	the	DET
fcis-13848	19	50	life	life	NOUN
fcis-13848	19	51	-	-	PUNCT
fcis-13848	19	52	saving	save	VERB
fcis-13848	19	53	effect	effect	NOUN
fcis-13848	19	54	and	and	CCONJ
fcis-13848	19	55	success	success	NOUN
fcis-13848	19	56	rate	rate	NOUN
fcis-13848	19	57	[	[	X
fcis-13848	19	58	3,4	3,4	NUM
fcis-13848	19	59	]	]	PUNCT
fcis-13848	19	60	.	.	PUNCT
fcis-13848	20	1	in	in	ADP
fcis-13848	20	2	addition	addition	NOUN
fcis-13848	20	3	,	,	PUNCT
fcis-13848	20	4	the	the	DET
fcis-13848	20	5	study	study	NOUN
fcis-13848	20	6	can	can	AUX
fcis-13848	20	7	also	also	ADV
fcis-13848	20	8	be	be	AUX
fcis-13848	20	9	used	use	VERB
fcis-13848	20	10	for	for	ADP
fcis-13848	20	11	future	future	ADJ
fcis-13848	20	12	arrhythmia	arrhythmia	NOUN
fcis-13848	20	13	.	.	PUNCT
fcis-13848	21	1	this	this	DET
fcis-13848	21	2	study	study	NOUN
fcis-13848	21	3	provides	provide	VERB
fcis-13848	21	4	data	data	NOUN
fcis-13848	21	5	basis	basis	NOUN
fcis-13848	21	6	and	and	CCONJ
fcis-13848	21	7	analysis	analysis	NOUN
fcis-13848	21	8	method	method	NOUN
fcis-13848	21	9	reference	reference	NOUN
fcis-13848	21	10	,	,	PUNCT
fcis-13848	21	11	which	which	PRON
fcis-13848	21	12	is	be	AUX
fcis-13848	21	13	helpful	helpful	ADJ
fcis-13848	21	14	to	to	PART
fcis-13848	21	15	further	far	ADV
fcis-13848	21	16	explore	explore	VERB
fcis-13848	21	17	the	the	DET
fcis-13848	21	18	mechanism	mechanism	NOUN
fcis-13848	21	19	and	and	CCONJ
fcis-13848	21	20	treatment	treatment	NOUN
fcis-13848	21	21	of	of	ADP
fcis-13848	21	22	arrhythmia	arrhythmia	NOUN
fcis-13848	21	23	[	[	X
fcis-13848	21	24	5	5	NUM
fcis-13848	21	25	]	]	PUNCT
fcis-13848	21	26	.	.	PUNCT
fcis-13848	22	1	2	2	X
fcis-13848	22	2	.	.	X
fcis-13848	22	3	basic	basic	ADJ
fcis-13848	22	4	functions	function	NOUN
fcis-13848	22	5	of	of	ADP
fcis-13848	22	6	cnn	cnn	PROPN
fcis-13848	22	7	neural	neural	ADJ
fcis-13848	22	8	network	network	NOUN
fcis-13848	22	9	2.1	2.1	NUM
fcis-13848	22	10	.	.	PUNCT
fcis-13848	23	1	structure	structure	NOUN
fcis-13848	23	2	of	of	ADP
fcis-13848	23	3	cnn	cnn	PROPN
fcis-13848	23	4	neural	neural	ADJ
fcis-13848	23	5	network	network	NOUN
fcis-13848	23	6	in	in	ADP
fcis-13848	23	7	order	order	NOUN
fcis-13848	23	8	to	to	PART
fcis-13848	23	9	establish	establish	VERB
fcis-13848	23	10	a	a	DET
fcis-13848	23	11	deep	deep	ADJ
fcis-13848	23	12	neural	neural	ADJ
fcis-13848	23	13	network	network	NOUN
fcis-13848	23	14	model	model	NOUN
fcis-13848	23	15	that	that	PRON
fcis-13848	23	16	can	can	AUX
fcis-13848	23	17	classify	classify	VERB
fcis-13848	23	18	and	and	CCONJ
fcis-13848	23	19	predict	predict	VERB
fcis-13848	23	20	the	the	DET
fcis-13848	23	21	risk	risk	NOUN
fcis-13848	23	22	level	level	NOUN
fcis-13848	23	23	of	of	ADP
fcis-13848	23	24	arrhythmia	arrhythmia	NOUN
fcis-13848	23	25	,	,	PUNCT
fcis-13848	23	26	convolutional	convolutional	ADJ
fcis-13848	23	27	neural	neural	ADJ
fcis-13848	23	28	network	network	NOUN
fcis-13848	23	29	(	(	PUNCT
fcis-13848	23	30	cnn	cnn	PROPN
fcis-13848	23	31	)	)	PUNCT
fcis-13848	23	32	is	be	AUX
fcis-13848	23	33	used	use	VERB
fcis-13848	23	34	as	as	ADP
fcis-13848	23	35	the	the	DET
fcis-13848	23	36	main	main	ADJ
fcis-13848	23	37	architecture	architecture	NOUN
fcis-13848	23	38	.	.	PUNCT
fcis-13848	24	1	cnn	cnn	PROPN
fcis-13848	24	2	is	be	AUX
fcis-13848	24	3	particularly	particularly	ADV
fcis-13848	24	4	good	good	ADJ
fcis-13848	24	5	at	at	ADP
fcis-13848	24	6	processing	processing	NOUN
fcis-13848	24	7	data	datum	NOUN
fcis-13848	24	8	with	with	ADP
fcis-13848	24	9	spatial	spatial	ADJ
fcis-13848	24	10	structure	structure	NOUN
fcis-13848	24	11	,	,	PUNCT
fcis-13848	24	12	such	such	ADJ
fcis-13848	24	13	as	as	ADP
fcis-13848	24	14	images	image	NOUN
fcis-13848	24	15	and	and	CCONJ
fcis-13848	24	16	signals	signal	NOUN
fcis-13848	24	17	,	,	PUNCT
fcis-13848	24	18	making	make	VERB
fcis-13848	24	19	it	it	PRON
fcis-13848	24	20	an	an	DET
fcis-13848	24	21	ideal	ideal	ADJ
fcis-13848	24	22	choice	choice	NOUN
fcis-13848	24	23	for	for	ADP
fcis-13848	24	24	analyzing	analyze	VERB
fcis-13848	24	25	electrocardiogram	electrocardiogram	NOUN
fcis-13848	24	26	(	(	PUNCT
fcis-13848	24	27	ecg	ecg	PROPN
fcis-13848	24	28	)	)	PUNCT
fcis-13848	24	29	data	datum	NOUN
fcis-13848	24	30	.	.	PUNCT
fcis-13848	25	1	for	for	ADP
fcis-13848	25	2	the	the	DET
fcis-13848	25	3	classification	classification	NOUN
fcis-13848	25	4	task	task	NOUN
fcis-13848	25	5	,	,	PUNCT
fcis-13848	25	6	the	the	DET
fcis-13848	25	7	softmax	softmax	NOUN
fcis-13848	25	8	function	function	NOUN
fcis-13848	25	9	can	can	AUX
fcis-13848	25	10	be	be	AUX
fcis-13848	25	11	used	use	VERB
fcis-13848	25	12	to	to	PART
fcis-13848	25	13	convert	convert	VERB
fcis-13848	25	14	the	the	DET
fcis-13848	25	15	output	output	NOUN
fcis-13848	25	16	of	of	ADP
fcis-13848	25	17	the	the	DET
fcis-13848	25	18	model	model	NOUN
fcis-13848	25	19	into	into	ADP
fcis-13848	25	20	a	a	DET
fcis-13848	25	21	probability	probability	NOUN
fcis-13848	25	22	distribution	distribution	NOUN
fcis-13848	25	23	,	,	PUNCT
fcis-13848	25	24	and	and	CCONJ
fcis-13848	25	25	the	the	DET
fcis-13848	25	26	cross	cross	ADJ
fcis-13848	25	27	-	-	ADJ
fcis-13848	25	28	entropy	entropy	ADJ
fcis-13848	25	29	loss	loss	NOUN
fcis-13848	25	30	function	function	NOUN
fcis-13848	25	31	is	be	AUX
fcis-13848	25	32	used	use	VERB
fcis-13848	25	33	for	for	ADP
fcis-13848	25	34	optimization	optimization	NOUN
fcis-13848	25	35	.	.	PUNCT
fcis-13848	26	1	the	the	DET
fcis-13848	26	2	network	network	NOUN
fcis-13848	26	3	structure	structure	NOUN
fcis-13848	26	4	is	be	AUX
fcis-13848	26	5	shown	show	VERB
fcis-13848	26	6	in	in	ADP
fcis-13848	26	7	figure	figure	NOUN
fcis-13848	26	8	1	1	NUM
fcis-13848	26	9	.	.	PUNCT
fcis-13848	26	10	figure	figure	NOUN
fcis-13848	26	11	1	1	NUM
fcis-13848	26	12	.	.	PUNCT
fcis-13848	26	13	neural	neural	ADJ
fcis-13848	26	14	network	network	NOUN
fcis-13848	26	15	structure	structure	NOUN
fcis-13848	26	16	the	the	DET
fcis-13848	26	17	cnn	cnn	PROPN
fcis-13848	26	18	model	model	NOUN
fcis-13848	26	19	is	be	AUX
fcis-13848	26	20	composed	compose	VERB
fcis-13848	26	21	of	of	ADP
fcis-13848	26	22	multiple	multiple	ADJ
fcis-13848	26	23	layers	layer	NOUN
fcis-13848	26	24	,	,	PUNCT
fcis-13848	26	25	including	include	VERB
fcis-13848	26	26	convolution	convolution	NOUN
fcis-13848	26	27	layer	layer	NOUN
fcis-13848	26	28	,	,	PUNCT
fcis-13848	26	29	pooling	pool	VERB
fcis-13848	26	30	layer	layer	NOUN
fcis-13848	26	31	,	,	PUNCT
fcis-13848	26	32	fully	fully	ADV
fcis-13848	26	33	connected	connected	ADJ
fcis-13848	26	34	layer	layer	NOUN
fcis-13848	26	35	and	and	CCONJ
fcis-13848	26	36	softmax	softmax	NOUN
fcis-13848	26	37	output	output	NOUN
fcis-13848	26	38	layer	layer	NOUN
fcis-13848	26	39	.	.	PUNCT
fcis-13848	27	1	the	the	DET
fcis-13848	27	2	model	model	NOUN
fcis-13848	27	3	takes	take	VERB
fcis-13848	27	4	the	the	DET
fcis-13848	27	5	original	original	ADJ
fcis-13848	27	6	ecg	ecg	PROPN
fcis-13848	27	7	time	time	NOUN
fcis-13848	27	8	series	series	NOUN
fcis-13848	27	9	as	as	ADP
fcis-13848	27	10	its	its	PRON
fcis-13848	27	11	input	input	NOUN
fcis-13848	27	12	and	and	CCONJ
fcis-13848	27	13	provides	provide	VERB
fcis-13848	27	14	a	a	DET
fcis-13848	27	15	prediction	prediction	NOUN
fcis-13848	27	16	label	label	NOUN
fcis-13848	27	17	every	every	DET
fcis-13848	27	18	second	second	NOUN
fcis-13848	27	19	.	.	PUNCT
fcis-13848	28	1	the	the	DET
fcis-13848	28	2	basic	basic	ADJ
fcis-13848	28	3	model	model	NOUN
fcis-13848	28	4	of	of	ADP
fcis-13848	28	5	cnn	cnn	PROPN
fcis-13848	28	6	includes	include	VERB
fcis-13848	28	7	several	several	ADJ
fcis-13848	28	8	main	main	ADJ
fcis-13848	28	9	components	component	NOUN
fcis-13848	28	10	:	:	PUNCT
fcis-13848	28	11	(	(	PUNCT
fcis-13848	28	12	1	1	X
fcis-13848	28	13	)	)	PUNCT
fcis-13848	28	14	convolution	convolution	NOUN
fcis-13848	28	15	layer	layer	NOUN
fcis-13848	28	16	,	,	PUNCT
fcis-13848	28	17	the	the	DET
fcis-13848	28	18	convolution	convolution	NOUN
fcis-13848	28	19	operation	operation	NOUN
fcis-13848	28	20	is	be	AUX
fcis-13848	28	21	applied	apply	VERB
fcis-13848	28	22	to	to	ADP
fcis-13848	28	23	the	the	DET
fcis-13848	28	24	input	input	NOUN
fcis-13848	28	25	data	datum	NOUN
fcis-13848	28	26	to	to	PART
fcis-13848	28	27	capture	capture	VERB
fcis-13848	28	28	the	the	DET
fcis-13848	28	29	local	local	ADJ
fcis-13848	28	30	mode	mode	NOUN
fcis-13848	28	31	.	.	PUNCT
fcis-13848	29	1	(	(	PUNCT
fcis-13848	29	2	2	2	X
fcis-13848	29	3	)	)	PUNCT
fcis-13848	29	4	pooling	pool	VERB
fcis-13848	29	5	layer	layer	NOUN
fcis-13848	29	6	,	,	PUNCT
fcis-13848	29	7	which	which	PRON
fcis-13848	29	8	reduces	reduce	VERB
fcis-13848	29	9	the	the	DET
fcis-13848	29	10	spatial	spatial	ADJ
fcis-13848	29	11	dimension	dimension	NOUN
fcis-13848	29	12	of	of	ADP
fcis-13848	29	13	data	datum	NOUN
fcis-13848	29	14	while	while	SCONJ
fcis-13848	29	15	retaining	retain	VERB
fcis-13848	29	16	important	important	ADJ
fcis-13848	29	17	features	feature	NOUN
fcis-13848	29	18	.	.	PUNCT
fcis-13848	30	1	(	(	PUNCT
fcis-13848	30	2	3	3	X
fcis-13848	30	3	)	)	PUNCT
fcis-13848	30	4	fully	fully	ADV
fcis-13848	30	5	connected	connect	VERB
fcis-13848	30	6	layer	layer	NOUN
fcis-13848	30	7	,	,	PUNCT
fcis-13848	30	8	interpret	interpret	VERB
fcis-13848	30	9	features	feature	NOUN
fcis-13848	30	10	and	and	CCONJ
fcis-13848	30	11	make	make	VERB
fcis-13848	30	12	predictions	prediction	NOUN
fcis-13848	30	13	.	.	PUNCT
fcis-13848	31	1	(	(	PUNCT
fcis-13848	31	2	4	4	X
fcis-13848	31	3	)	)	PUNCT
fcis-13848	31	4	softmax	softmax	NOUN
fcis-13848	31	5	layer	layer	NOUN
fcis-13848	31	6	,	,	PUNCT
fcis-13848	31	7	which	which	PRON
fcis-13848	31	8	converts	convert	VERB
fcis-13848	31	9	the	the	DET
fcis-13848	31	10	output	output	NOUN
fcis-13848	31	11	of	of	ADP
fcis-13848	31	12	the	the	DET
fcis-13848	31	13	model	model	NOUN
fcis-13848	31	14	into	into	ADP
fcis-13848	31	15	a	a	DET
fcis-13848	31	16	probability	probability	NOUN
fcis-13848	31	17	distribution	distribution	NOUN
fcis-13848	31	18	for	for	ADP
fcis-13848	31	19	classification	classification	NOUN
fcis-13848	31	20	.	.	PUNCT
fcis-13848	32	1	for	for	ADP
fcis-13848	32	2	the	the	DET
fcis-13848	32	3	regression	regression	NOUN
fcis-13848	32	4	problem	problem	NOUN
fcis-13848	32	5	,	,	PUNCT
fcis-13848	32	6	we	we	PRON
fcis-13848	32	7	can	can	AUX
fcis-13848	32	8	optimize	optimize	VERB
fcis-13848	32	9	the	the	DET
fcis-13848	32	10	mean	mean	ADJ
fcis-13848	32	11	square	square	ADJ
fcis-13848	32	12	error	error	NOUN
fcis-13848	32	13	loss	loss	NOUN
fcis-13848	32	14	function	function	NOUN
fcis-13848	32	15	.	.	PUNCT
fcis-13848	33	1	in	in	ADP
fcis-13848	33	2	addition	addition	NOUN
fcis-13848	33	3	,	,	PUNCT
fcis-13848	33	4	we	we	PRON
fcis-13848	33	5	can	can	AUX
fcis-13848	33	6	use	use	VERB
fcis-13848	33	7	the	the	DET
fcis-13848	33	8	stochastic	stochastic	ADJ
fcis-13848	33	9	gradient	gradient	ADJ
fcis-13848	33	10	descent	descent	NOUN
fcis-13848	33	11	(	(	PUNCT
fcis-13848	33	12	sgd	sgd	NOUN
fcis-13848	33	13	)	)	PUNCT
fcis-13848	33	14	algorithm	algorithm	NOUN
fcis-13848	33	15	or	or	CCONJ
fcis-13848	33	16	its	its	PRON
fcis-13848	33	17	variants	variant	NOUN
fcis-13848	33	18	44	44	NUM
fcis-13848	33	19	(	(	PUNCT
fcis-13848	33	20	such	such	ADJ
fcis-13848	33	21	as	as	ADP
fcis-13848	33	22	adam	adam	PROPN
fcis-13848	33	23	)	)	PUNCT
fcis-13848	33	24	to	to	PART
fcis-13848	33	25	train	train	VERB
fcis-13848	33	26	the	the	DET
fcis-13848	33	27	model	model	NOUN
fcis-13848	33	28	,	,	PUNCT
fcis-13848	33	29	and	and	CCONJ
fcis-13848	33	30	evaluate	evaluate	VERB
fcis-13848	33	31	the	the	DET
fcis-13848	33	32	performance	performance	NOUN
fcis-13848	33	33	of	of	ADP
fcis-13848	33	34	the	the	DET
fcis-13848	33	35	model	model	NOUN
fcis-13848	33	36	by	by	ADP
fcis-13848	33	37	cross	cross	NOUN
fcis-13848	33	38	-	-	ADJ
fcis-13848	33	39	validation	validation	ADJ
fcis-13848	33	40	or	or	CCONJ
fcis-13848	33	41	leave	leave	VERB
fcis-13848	33	42	-	-	PUNCT
fcis-13848	33	43	out	out	ADP
fcis-13848	33	44	method	method	NOUN
fcis-13848	33	45	.	.	PUNCT
fcis-13848	34	1	specifically	specifically	ADV
fcis-13848	34	2	,	,	PUNCT
fcis-13848	34	3	set	set	VERB
fcis-13848	34	4	the	the	DET
fcis-13848	34	5	ecg	ecg	PROPN
fcis-13848	34	6	data	datum	NOUN
fcis-13848	34	7	shown	show	VERB
fcis-13848	34	8	in	in	ADP
fcis-13848	34	9	figure	figure	NOUN
fcis-13848	34	10	2	2	NUM
fcis-13848	34	11	,	,	PUNCT
fcis-13848	34	12	and	and	CCONJ
fcis-13848	34	13	use	use	VERB
fcis-13848	34	14	cnn	cnn	PROPN
fcis-13848	34	15	to	to	PART
fcis-13848	34	16	process	process	VERB
fcis-13848	34	17	the	the	DET
fcis-13848	34	18	ecg	ecg	PROPN
fcis-13848	34	19	data	data	PROPN
fcis-13848	34	20	flow	flow	NOUN
fcis-13848	34	21	chart	chart	NOUN
fcis-13848	34	22	,	,	PUNCT
fcis-13848	34	23	where	where	SCONJ
fcis-13848	34	24	each	each	DET
fcis-13848	34	25	point	point	NOUN
fcis-13848	34	26	represents	represent	VERB
fcis-13848	34	27	the	the	DET
fcis-13848	34	28	ecg	ecg	PROPN
fcis-13848	34	29	signal	signal	NOUN
fcis-13848	34	30	at	at	ADP
fcis-13848	34	31	a	a	DET
fcis-13848	34	32	specific	specific	ADJ
fcis-13848	34	33	time	time	NOUN
fcis-13848	34	34	.	.	PUNCT
fcis-13848	35	1	labels	label	NOUN
fcis-13848	35	2	are	be	AUX
fcis-13848	35	3	set	set	VERB
fcis-13848	35	4	as	as	SCONJ
fcis-13848	35	5	shown	show	VERB
fcis-13848	35	6	in	in	ADP
fcis-13848	35	7	figure	figure	NOUN
fcis-13848	35	8	2	2	NUM
fcis-13848	35	9	,	,	PUNCT
fcis-13848	35	10	where	where	SCONJ
fcis-13848	35	11	each	each	DET
fcis-13848	35	12	label	label	NOUN
fcis-13848	35	13	represents	represent	VERB
fcis-13848	35	14	the	the	DET
fcis-13848	35	15	type	type	NOUN
fcis-13848	35	16	or	or	CCONJ
fcis-13848	35	17	degree	degree	NOUN
fcis-13848	35	18	of	of	ADP
fcis-13848	35	19	arrhythmia	arrhythmia	NOUN
fcis-13848	35	20	risk	risk	NOUN
fcis-13848	35	21	.	.	PUNCT
fcis-13848	36	1	our	our	PRON
fcis-13848	36	2	goal	goal	NOUN
fcis-13848	36	3	is	be	AUX
fcis-13848	36	4	to	to	PART
fcis-13848	36	5	learn	learn	VERB
fcis-13848	36	6	a	a	DET
fcis-13848	36	7	function	function	NOUN
fcis-13848	36	8	that	that	PRON
fcis-13848	36	9	maps	map	VERB
fcis-13848	36	10	the	the	DET
fcis-13848	36	11	input	input	NOUN
fcis-13848	36	12	ecg	ecg	PROPN
fcis-13848	36	13	data	datum	NOUN
fcis-13848	36	14	to	to	ADP
fcis-13848	36	15	the	the	DET
fcis-13848	36	16	output	output	NOUN
fcis-13848	36	17	label	label	NOUN
fcis-13848	36	18	y	y	PROPN
fcis-13848	36	19	,	,	PUNCT
fcis-13848	36	20	as	as	SCONJ
fcis-13848	36	21	shown	show	VERB
fcis-13848	36	22	in	in	ADP
fcis-13848	36	23	figure	figure	NOUN
fcis-13848	36	24	2	2	NUM
fcis-13848	36	25	.	.	PUNCT
fcis-13848	37	1	we	we	PRON
fcis-13848	37	2	can	can	AUX
fcis-13848	37	3	model	model	VERB
fcis-13848	37	4	cnn	cnn	PROPN
fcis-13848	37	5	as	as	ADP
fcis-13848	37	6	a	a	DET
fcis-13848	37	7	multi	multi	ADJ
fcis-13848	37	8	-	-	ADJ
fcis-13848	37	9	layer	layer	ADJ
fcis-13848	37	10	network	network	NOUN
fcis-13848	37	11	,	,	PUNCT
fcis-13848	37	12	as	as	SCONJ
fcis-13848	37	13	shown	show	VERB
fcis-13848	37	14	in	in	ADP
fcis-13848	37	15	figure	figure	NOUN
fcis-13848	37	16	2	2	NUM
fcis-13848	37	17	.	.	PUNCT
fcis-13848	38	1	each	each	DET
fcis-13848	38	2	layer	layer	NOUN
fcis-13848	38	3	contains	contain	VERB
fcis-13848	38	4	a	a	DET
fcis-13848	38	5	convolution	convolution	NOUN
fcis-13848	38	6	layer	layer	NOUN
fcis-13848	38	7	,	,	PUNCT
fcis-13848	38	8	a	a	DET
fcis-13848	38	9	pooling	pooling	NOUN
fcis-13848	38	10	layer	layer	NOUN
fcis-13848	38	11	and	and	CCONJ
fcis-13848	38	12	an	an	DET
fcis-13848	38	13	activation	activation	NOUN
fcis-13848	38	14	function	function	NOUN
fcis-13848	38	15	layer	layer	NOUN
fcis-13848	38	16	.	.	PUNCT
fcis-13848	39	1	the	the	DET
fcis-13848	39	2	convolution	convolution	NOUN
fcis-13848	39	3	layer	layer	NOUN
fcis-13848	39	4	is	be	AUX
fcis-13848	39	5	used	use	VERB
fcis-13848	39	6	to	to	PART
fcis-13848	39	7	extract	extract	VERB
fcis-13848	39	8	features	feature	NOUN
fcis-13848	39	9	,	,	PUNCT
fcis-13848	39	10	the	the	DET
fcis-13848	39	11	pooling	pool	VERB
fcis-13848	39	12	layer	layer	NOUN
fcis-13848	39	13	is	be	AUX
fcis-13848	39	14	used	use	VERB
fcis-13848	39	15	to	to	PART
fcis-13848	39	16	reduce	reduce	VERB
fcis-13848	39	17	the	the	DET
fcis-13848	39	18	feature	feature	NOUN
fcis-13848	39	19	dimension	dimension	NOUN
fcis-13848	39	20	,	,	PUNCT
fcis-13848	39	21	and	and	CCONJ
fcis-13848	39	22	the	the	DET
fcis-13848	39	23	activation	activation	NOUN
fcis-13848	39	24	function	function	NOUN
fcis-13848	39	25	layer	layer	NOUN
fcis-13848	39	26	is	be	AUX
fcis-13848	39	27	used	use	VERB
fcis-13848	39	28	to	to	PART
fcis-13848	39	29	increase	increase	VERB
fcis-13848	39	30	the	the	DET
fcis-13848	39	31	nonlinear	nonlinear	ADJ
fcis-13848	39	32	ability	ability	NOUN
fcis-13848	39	33	of	of	ADP
fcis-13848	39	34	the	the	DET
fcis-13848	39	35	model	model	NOUN
fcis-13848	39	36	.	.	PUNCT
fcis-13848	40	1	specifically	specifically	ADV
fcis-13848	40	2	,	,	PUNCT
fcis-13848	40	3	if	if	SCONJ
fcis-13848	40	4	the	the	DET
fcis-13848	40	5	input	input	NOUN
fcis-13848	40	6	of	of	ADP
fcis-13848	40	7	the	the	DET
fcis-13848	40	8	layer	layer	NOUN
fcis-13848	40	9	is	be	AUX
fcis-13848	40	10	shown	show	VERB
fcis-13848	40	11	in	in	ADP
fcis-13848	40	12	figure	figure	NOUN
fcis-13848	40	13	2	2	NUM
fcis-13848	40	14	,	,	PUNCT
fcis-13848	40	15	and	and	CCONJ
fcis-13848	40	16	the	the	DET
fcis-13848	40	17	output	output	NOUN
fcis-13848	40	18	of	of	ADP
fcis-13848	40	19	the	the	DET
fcis-13848	40	20	layer	layer	NOUN
fcis-13848	40	21	is	be	AUX
fcis-13848	40	22	also	also	ADV
fcis-13848	40	23	shown	show	VERB
fcis-13848	40	24	in	in	ADP
fcis-13848	40	25	figure	figure	NOUN
fcis-13848	40	26	2	2	NUM
fcis-13848	40	27	,	,	PUNCT
fcis-13848	40	28	the	the	DET
fcis-13848	40	29	calculation	calculation	NOUN
fcis-13848	40	30	of	of	ADP
fcis-13848	40	31	the	the	DET
fcis-13848	40	32	layer	layer	NOUN
fcis-13848	40	33	can	can	AUX
fcis-13848	40	34	be	be	AUX
fcis-13848	40	35	expressed	express	VERB
fcis-13848	40	36	as	as	ADP
fcis-13848	40	37	the	the	DET
fcis-13848	40	38	details	detail	NOUN
fcis-13848	40	39	in	in	ADP
fcis-13848	40	40	figure	figure	NOUN
fcis-13848	40	41	2	2	NUM
fcis-13848	40	42	.	.	PUNCT
fcis-13848	40	43	∗	∗	NOUN
fcis-13848	40	44	(	(	PUNCT
fcis-13848	40	45	1	1	NUM
fcis-13848	40	46	)	)	PUNCT
fcis-13848	40	47	figure	figure	NOUN
fcis-13848	40	48	2	2	NUM
fcis-13848	40	49	.	.	PUNCT
fcis-13848	41	1	ecg	ecg	PROPN
fcis-13848	41	2	data	data	PROPN
fcis-13848	41	3	processing	processing	NOUN
fcis-13848	41	4	flow	flow	NOUN
fcis-13848	41	5	chart	chart	NOUN
fcis-13848	41	6	using	use	VERB
fcis-13848	41	7	cnn	cnn	PROPN
fcis-13848	41	8	*	*	PUNCT
fcis-13848	41	9	represents	represent	VERB
fcis-13848	41	10	the	the	DET
fcis-13848	41	11	convolution	convolution	NOUN
fcis-13848	41	12	operation	operation	NOUN
fcis-13848	41	13	and	and	CCONJ
fcis-13848	41	14	represents	represent	VERB
fcis-13848	41	15	the	the	DET
fcis-13848	41	16	activation	activation	NOUN
fcis-13848	41	17	function	function	NOUN
fcis-13848	41	18	.	.	PUNCT
fcis-13848	42	1	the	the	DET
fcis-13848	42	2	final	final	ADJ
fcis-13848	42	3	output	output	NOUN
fcis-13848	42	4	z	z	NOUN
fcis-13848	42	5	(	(	PUNCT
fcis-13848	42	6	can	can	AUX
fcis-13848	42	7	be	be	AUX
fcis-13848	42	8	mapped	map	VERB
fcis-13848	42	9	to	to	ADP
fcis-13848	42	10	the	the	DET
fcis-13848	42	11	label	label	NOUN
fcis-13848	42	12	space	space	NOUN
fcis-13848	42	13	through	through	ADP
fcis-13848	42	14	the	the	DET
fcis-13848	42	15	fully	fully	ADV
fcis-13848	42	16	connected	connect	VERB
fcis-13848	42	17	layer	layer	NOUN
fcis-13848	42	18	,	,	PUNCT
fcis-13848	42	19	and	and	CCONJ
fcis-13848	42	20	then	then	ADV
fcis-13848	42	21	use	use	VERB
fcis-13848	42	22	the	the	DET
fcis-13848	42	23	softmax	softmax	NOUN
fcis-13848	42	24	function	function	NOUN
fcis-13848	42	25	to	to	PART
fcis-13848	42	26	convert	convert	VERB
fcis-13848	42	27	the	the	DET
fcis-13848	42	28	output	output	NOUN
fcis-13848	42	29	to	to	ADP
fcis-13848	42	30	a	a	DET
fcis-13848	42	31	probability	probability	NOUN
fcis-13848	42	32	distribution	distribution	NOUN
fcis-13848	42	33	.	.	PUNCT
fcis-13848	43	1	for	for	ADP
fcis-13848	43	2	the	the	DET
fcis-13848	43	3	classification	classification	NOUN
fcis-13848	43	4	problem	problem	NOUN
fcis-13848	43	5	,	,	PUNCT
fcis-13848	43	6	we	we	PRON
fcis-13848	43	7	can	can	AUX
fcis-13848	43	8	use	use	VERB
fcis-13848	43	9	the	the	DET
fcis-13848	43	10	cross	cross	NOUN
fcis-13848	43	11	entropy	entropy	PROPN
fcis-13848	43	12	loss	loss	NOUN
fcis-13848	43	13	function	function	NOUN
fcis-13848	43	14	to	to	PART
fcis-13848	43	15	optimize	optimize	VERB
fcis-13848	43	16	the	the	DET
fcis-13848	43	17	model	model	NOUN
fcis-13848	43	18	:	:	PUNCT
fcis-13848	43	19	∑	∑	ADP
fcis-13848	43	20	∑	∑	PROPN
fcis-13848	43	21	y	y	PROPN
fcis-13848	43	22	logy	logy	NOUN
fcis-13848	43	23	(	(	PUNCT
fcis-13848	43	24	2	2	NUM
fcis-13848	43	25	)	)	PUNCT
fcis-13848	43	26	where	where	SCONJ
fcis-13848	43	27	m	m	NOUN
fcis-13848	43	28	represents	represent	VERB
fcis-13848	43	29	the	the	DET
fcis-13848	43	30	number	number	NOUN
fcis-13848	43	31	of	of	ADP
fcis-13848	43	32	samples	sample	NOUN
fcis-13848	43	33	,	,	PUNCT
fcis-13848	43	34	people	people	NOUN
fcis-13848	43	35	represent	represent	VERB
fcis-13848	43	36	the	the	DET
fcis-13848	43	37	number	number	NOUN
fcis-13848	43	38	of	of	ADP
fcis-13848	43	39	categories	category	NOUN
fcis-13848	43	40	,	,	PUNCT
fcis-13848	43	41	and	and	CCONJ
fcis-13848	43	42	y	y	PROPN
fcis-13848	43	43	represents	represent	VERB
fcis-13848	43	44	the	the	DET
fcis-13848	43	45	first	first	ADJ
fcis-13848	43	46	category	category	NOUN
fcis-13848	43	47	label	label	NOUN
fcis-13848	43	48	0	0	NUM
fcis-13848	43	49	or	or	CCONJ
fcis-13848	43	50	1	1	NUM
fcis-13848	43	51	of	of	ADP
fcis-13848	43	52	the	the	DET
fcis-13848	43	53	first	first	ADJ
fcis-13848	43	54	sample	sample	NOUN
fcis-13848	43	55	)	)	PUNCT
fcis-13848	43	56	,	,	PUNCT
fcis-13848	43	57	which	which	PRON
fcis-13848	43	58	represents	represent	VERB
fcis-13848	43	59	the	the	DET
fcis-13848	43	60	prediction	prediction	NOUN
fcis-13848	43	61	probability	probability	NOUN
fcis-13848	43	62	of	of	ADP
fcis-13848	43	63	the	the	DET
fcis-13848	43	64	model	model	NOUN
fcis-13848	43	65	.	.	PUNCT
fcis-13848	44	1	for	for	ADP
fcis-13848	44	2	the	the	DET
fcis-13848	44	3	regression	regression	NOUN
fcis-13848	44	4	problem	problem	NOUN
fcis-13848	44	5	,	,	PUNCT
fcis-13848	44	6	we	we	PRON
fcis-13848	44	7	can	can	AUX
fcis-13848	44	8	use	use	VERB
fcis-13848	44	9	the	the	DET
fcis-13848	44	10	mean	mean	ADJ
fcis-13848	44	11	square	square	ADJ
fcis-13848	44	12	error	error	NOUN
fcis-13848	44	13	loss	loss	NOUN
fcis-13848	44	14	function	function	NOUN
fcis-13848	44	15	to	to	PART
fcis-13848	44	16	optimize	optimize	VERB
fcis-13848	44	17	.	.	PUNCT
fcis-13848	45	1	furthermore	furthermore	ADV
fcis-13848	45	2	,	,	PUNCT
fcis-13848	45	3	we	we	PRON
fcis-13848	45	4	need	need	VERB
fcis-13848	45	5	to	to	PART
fcis-13848	45	6	determine	determine	VERB
fcis-13848	45	7	the	the	DET
fcis-13848	45	8	weight	weight	NOUN
fcis-13848	45	9	of	of	ADP
fcis-13848	45	10	each	each	DET
fcis-13848	45	11	factor	factor	NOUN
fcis-13848	45	12	.	.	PUNCT
fcis-13848	46	1	in	in	ADP
fcis-13848	46	2	general	general	ADJ
fcis-13848	46	3	,	,	PUNCT
fcis-13848	46	4	the	the	DET
fcis-13848	46	5	factors	factor	NOUN
fcis-13848	46	6	in	in	ADP
fcis-13848	46	7	the	the	DET
fcis-13848	46	8	factor	factor	NOUN
fcis-13848	46	9	set	set	VERB
fcis-13848	46	10	play	play	VERB
fcis-13848	46	11	different	different	ADJ
fcis-13848	46	12	roles	role	NOUN
fcis-13848	46	13	in	in	ADP
fcis-13848	46	14	the	the	DET
fcis-13848	46	15	comprehensive	comprehensive	ADJ
fcis-13848	46	16	evaluation	evaluation	NOUN
fcis-13848	46	17	.	.	PUNCT
fcis-13848	47	1	the	the	DET
fcis-13848	47	2	comprehensive	comprehensive	ADJ
fcis-13848	47	3	evaluation	evaluation	NOUN
fcis-13848	47	4	results	result	NOUN
fcis-13848	47	5	are	be	AUX
fcis-13848	47	6	not	not	PART
fcis-13848	47	7	only	only	ADV
fcis-13848	47	8	related	relate	VERB
fcis-13848	47	9	to	to	ADP
fcis-13848	47	10	the	the	DET
fcis-13848	47	11	evaluation	evaluation	NOUN
fcis-13848	47	12	of	of	ADP
fcis-13848	47	13	each	each	DET
fcis-13848	47	14	factor	factor	NOUN
fcis-13848	47	15	,	,	PUNCT
fcis-13848	47	16	but	but	CCONJ
fcis-13848	47	17	also	also	ADV
fcis-13848	47	18	to	to	ADP
fcis-13848	47	19	a	a	DET
fcis-13848	47	20	large	large	ADJ
fcis-13848	47	21	extent	extent	NOUN
fcis-13848	47	22	.	.	PUNCT
fcis-13848	48	1	it	it	PRON
fcis-13848	48	2	also	also	ADV
fcis-13848	48	3	depends	depend	VERB
fcis-13848	48	4	on	on	ADP
fcis-13848	48	5	the	the	DET
fcis-13848	48	6	role	role	NOUN
fcis-13848	48	7	of	of	ADP
fcis-13848	48	8	each	each	DET
fcis-13848	48	9	factor	factor	NOUN
fcis-13848	48	10	in	in	ADP
fcis-13848	48	11	the	the	DET
fcis-13848	48	12	comprehensive	comprehensive	ADJ
fcis-13848	48	13	evaluation	evaluation	NOUN
fcis-13848	48	14	.	.	PUNCT
fcis-13848	49	1	this	this	PRON
fcis-13848	49	2	requires	require	VERB
fcis-13848	49	3	the	the	DET
fcis-13848	49	4	determination	determination	NOUN
fcis-13848	49	5	of	of	ADP
fcis-13848	49	6	the	the	DET
fcis-13848	49	7	weight	weight	NOUN
fcis-13848	49	8	distribution	distribution	NOUN
fcis-13848	49	9	between	between	ADP
fcis-13848	49	10	each	each	DET
fcis-13848	49	11	factor	factor	NOUN
fcis-13848	49	12	.	.	PUNCT
fcis-13848	50	1	it	it	PRON
fcis-13848	50	2	is	be	AUX
fcis-13848	50	3	a	a	DET
fcis-13848	50	4	fuzzy	fuzzy	ADJ
fcis-13848	50	5	vector	vector	NOUN
fcis-13848	50	6	on	on	ADP
fcis-13848	50	7	u	u	NOUN
fcis-13848	50	8	,	,	PUNCT
fcis-13848	50	9	denoted	denote	VERB
fcis-13848	50	10	as	as	ADP
fcis-13848	50	11	:	:	PUNCT
fcis-13848	50	12	a	a	PRON
fcis-13848	50	13	=	=	X
fcis-13848	50	14	[	[	PUNCT
fcis-13848	50	15	a1	a1	PROPN
fcis-13848	50	16	,	,	PUNCT
fcis-13848	50	17	a2	a2	PROPN
fcis-13848	50	18	,	,	PUNCT
fcis-13848	50	19	a3	a3	NOUN
fcis-13848	50	20	,	,	PUNCT
fcis-13848	50	21	a4	a4	X
fcis-13848	50	22	]	]	PUNCT
fcis-13848	50	23	(	(	PUNCT
fcis-13848	50	24	3	3	X
fcis-13848	50	25	)	)	PUNCT
fcis-13848	51	1	where	where	SCONJ
fcis-13848	51	2	:	:	PUNCT
fcis-13848	51	3	ai	ai	VERB
fcis-13848	51	4	is	be	AUX
fcis-13848	51	5	the	the	DET
fcis-13848	51	6	weight	weight	NOUN
fcis-13848	51	7	of	of	ADP
fcis-13848	51	8	the	the	DET
fcis-13848	51	9	i	i	PROPN
fcis-13848	51	10	th	th	X
fcis-13848	51	11	factor	factor	NOUN
fcis-13848	51	12	and	and	CCONJ
fcis-13848	51	13	satisfies	satisfie	NOUN
fcis-13848	51	14	∑	∑	ADV
fcis-13848	51	15	ai	ai	VERB
fcis-13848	51	16	=	=	ADJ
fcis-13848	51	17	1	1	X
fcis-13848	51	18	.	.	PUNCT
fcis-13848	52	1	if	if	SCONJ
fcis-13848	52	2	the	the	DET
fcis-13848	52	3	sum	sum	NOUN
fcis-13848	52	4	does	do	AUX
fcis-13848	52	5	not	not	PART
fcis-13848	52	6	satisfy	satisfy	VERB
fcis-13848	52	7	the	the	DET
fcis-13848	52	8	sum	sum	NOUN
fcis-13848	52	9	of	of	ADP
fcis-13848	52	10	1	1	NUM
fcis-13848	52	11	,	,	PUNCT
fcis-13848	52	12	then	then	ADV
fcis-13848	52	13	normalization	normalization	NOUN
fcis-13848	52	14	can	can	AUX
fcis-13848	52	15	be	be	AUX
fcis-13848	52	16	achieved	achieve	VERB
fcis-13848	52	17	here	here	ADV
fcis-13848	52	18	or	or	CCONJ
fcis-13848	52	19	at	at	ADP
fcis-13848	52	20	the	the	DET
fcis-13848	52	21	final	final	ADJ
fcis-13848	52	22	result	result	NOUN
fcis-13848	52	23	.	.	PUNCT
fcis-13848	53	1	by	by	ADP
fcis-13848	53	2	using	use	VERB
fcis-13848	53	3	the	the	DET
fcis-13848	53	4	powerful	powerful	ADJ
fcis-13848	53	5	functions	function	NOUN
fcis-13848	53	6	of	of	ADP
fcis-13848	53	7	cnn	cnn	PROPN
fcis-13848	53	8	,	,	PUNCT
fcis-13848	53	9	the	the	DET
fcis-13848	53	10	model	model	NOUN
fcis-13848	53	11	can	can	AUX
fcis-13848	53	12	ensure	ensure	VERB
fcis-13848	53	13	accurate	accurate	ADJ
fcis-13848	53	14	and	and	CCONJ
fcis-13848	53	15	effective	effective	ADJ
fcis-13848	53	16	analysis	analysis	NOUN
fcis-13848	53	17	of	of	ADP
fcis-13848	53	18	ecg	ecg	PROPN
fcis-13848	53	19	data	data	PROPN
fcis-13848	53	20	,	,	PUNCT
fcis-13848	53	21	paving	pave	VERB
fcis-13848	53	22	the	the	DET
fcis-13848	53	23	way	way	NOUN
fcis-13848	53	24	for	for	ADP
fcis-13848	53	25	accurate	accurate	ADJ
fcis-13848	53	26	arrhythmia	arrhythmia	NOUN
fcis-13848	53	27	risk	risk	NOUN
fcis-13848	53	28	prediction	prediction	NOUN
fcis-13848	53	29	.	.	PUNCT
fcis-13848	54	1	as	as	SCONJ
fcis-13848	54	2	shown	show	VERB
fcis-13848	54	3	in	in	ADP
fcis-13848	54	4	figure	figure	NOUN
fcis-13848	54	5	3	3	NUM
fcis-13848	54	6	,	,	PUNCT
fcis-13848	54	7	the	the	DET
fcis-13848	54	8	flow	flow	NOUN
fcis-13848	54	9	chart	chart	NOUN
fcis-13848	54	10	of	of	ADP
fcis-13848	54	11	ecg	ecg	PROPN
fcis-13848	54	12	data	datum	NOUN
fcis-13848	54	13	analysis	analysis	NOUN
fcis-13848	54	14	and	and	CCONJ
fcis-13848	54	15	arrhythmia	arrhythmia	NOUN
fcis-13848	54	16	risk	risk	NOUN
fcis-13848	54	17	prediction	prediction	NOUN
fcis-13848	54	18	based	base	VERB
fcis-13848	54	19	on	on	ADP
fcis-13848	54	20	entropy	entropy	NOUN
fcis-13848	54	21	weight	weight	NOUN
fcis-13848	54	22	method	method	NOUN
fcis-13848	54	23	.	.	PUNCT
fcis-13848	55	1	there	there	PRON
fcis-13848	55	2	are	be	VERB
fcis-13848	55	3	many	many	ADJ
fcis-13848	55	4	methods	method	NOUN
fcis-13848	55	5	to	to	PART
fcis-13848	55	6	determine	determine	VERB
fcis-13848	55	7	the	the	DET
fcis-13848	55	8	weight	weight	NOUN
fcis-13848	55	9	of	of	ADP
fcis-13848	55	10	factors	factor	NOUN
fcis-13848	55	11	.	.	PUNCT
fcis-13848	56	1	this	this	DET
fcis-13848	56	2	paper	paper	NOUN
fcis-13848	56	3	adopts	adopt	VERB
fcis-13848	56	4	the	the	DET
fcis-13848	56	5	entropy	entropy	NOUN
fcis-13848	56	6	weight	weight	NOUN
fcis-13848	56	7	method	method	NOUN
fcis-13848	56	8	.	.	PUNCT
fcis-13848	57	1	the	the	DET
fcis-13848	57	2	entropy	entropy	PROPN
fcis-13848	57	3	weight	weight	NOUN
fcis-13848	57	4	method	method	NOUN
fcis-13848	57	5	is	be	AUX
fcis-13848	57	6	to	to	PART
fcis-13848	57	7	assign	assign	VERB
fcis-13848	57	8	weights	weight	NOUN
fcis-13848	57	9	according	accord	VERB
fcis-13848	57	10	to	to	ADP
fcis-13848	57	11	the	the	DET
fcis-13848	57	12	degree	degree	NOUN
fcis-13848	57	13	of	of	ADP
fcis-13848	57	14	change	change	NOUN
fcis-13848	57	15	of	of	ADP
fcis-13848	57	16	an	an	DET
fcis-13848	57	17	indicator	indicator	NOUN
fcis-13848	57	18	.	.	PUNCT
fcis-13848	58	1	then	then	ADV
fcis-13848	58	2	the	the	DET
fcis-13848	58	3	amount	amount	NOUN
fcis-13848	58	4	of	of	ADP
fcis-13848	58	5	information	information	NOUN
fcis-13848	58	6	is	be	AUX
fcis-13848	58	7	expressed	express	VERB
fcis-13848	58	8	by	by	ADP
fcis-13848	58	9	the	the	DET
fcis-13848	58	10	letter	letter	NOUN
fcis-13848	58	11	i	i	PRON
fcis-13848	58	12	,	,	PUNCT
fcis-13848	58	13	and	and	CCONJ
fcis-13848	58	14	the	the	DET
fcis-13848	58	15	probability	probability	NOUN
fcis-13848	58	16	is	be	AUX
fcis-13848	58	17	expressed	express	VERB
fcis-13848	58	18	by	by	ADP
fcis-13848	58	19	p.	p.	NOUN
fcis-13848	58	20	then	then	ADV
fcis-13848	58	21	we	we	PRON
fcis-13848	58	22	can	can	AUX
fcis-13848	58	23	establish	establish	VERB
fcis-13848	58	24	a	a	DET
fcis-13848	58	25	functional	functional	ADJ
fcis-13848	58	26	relationship	relationship	NOUN
fcis-13848	58	27	between	between	ADP
fcis-13848	58	28	them	they	PRON
fcis-13848	58	29	:	:	PUNCT
fcis-13848	58	30	figure	figure	NOUN
fcis-13848	58	31	3	3	NUM
fcis-13848	58	32	.	.	PUNCT
fcis-13848	59	1	ecg	ecg	PROPN
fcis-13848	59	2	data	datum	NOUN
fcis-13848	59	3	analysis	analysis	NOUN
fcis-13848	59	4	and	and	CCONJ
fcis-13848	59	5	arrhythmia	arrhythmia	NOUN
fcis-13848	59	6	risk	risk	NOUN
fcis-13848	59	7	prediction	prediction	NOUN
fcis-13848	59	8	flow	flow	NOUN
fcis-13848	59	9	chart	chart	NOUN
fcis-13848	59	10	based	base	VERB
fcis-13848	59	11	on	on	ADP
fcis-13848	59	12	entropy	entropy	NOUN
fcis-13848	59	13	weight	weight	NOUN
fcis-13848	59	14	method	method	NOUN
fcis-13848	59	15	3	3	NUM
fcis-13848	59	16	.	.	PUNCT
fcis-13848	60	1	the	the	DET
fcis-13848	60	2	establishment	establishment	NOUN
fcis-13848	60	3	of	of	ADP
fcis-13848	60	4	simulation	simulation	NOUN
fcis-13848	60	5	model	model	NOUN
fcis-13848	60	6	the	the	DET
fcis-13848	60	7	arrhythmia	arrhythmia	NOUN
fcis-13848	60	8	classification	classification	NOUN
fcis-13848	60	9	and	and	CCONJ
fcis-13848	60	10	risk	risk	NOUN
fcis-13848	60	11	prediction	prediction	NOUN
fcis-13848	60	12	model	model	NOUN
fcis-13848	60	13	are	be	AUX
fcis-13848	60	14	realized	realize	VERB
fcis-13848	60	15	in	in	ADP
fcis-13848	60	16	matlab	matlab	PROPN
fcis-13848	60	17	software	software	NOUN
fcis-13848	60	18	.	.	PUNCT
fcis-13848	61	1	3.1	3.1	NUM
fcis-13848	61	2	.	.	PUNCT
fcis-13848	61	3	analysis	analysis	NOUN
fcis-13848	61	4	of	of	ADP
fcis-13848	61	5	experimental	experimental	ADJ
fcis-13848	61	6	results	result	NOUN
fcis-13848	61	7	in	in	ADP
fcis-13848	61	8	this	this	DET
fcis-13848	61	9	study	study	NOUN
fcis-13848	61	10	,	,	PUNCT
fcis-13848	61	11	we	we	PRON
fcis-13848	61	12	developed	develop	VERB
fcis-13848	61	13	an	an	DET
fcis-13848	61	14	ecg	ecg	PROPN
fcis-13848	61	15	classification	classification	NOUN
fcis-13848	61	16	method	method	NOUN
fcis-13848	61	17	using	use	VERB
fcis-13848	61	18	region	region	NOUN
fcis-13848	61	19	-	-	PUNCT
fcis-13848	61	20	based	base	VERB
fcis-13848	61	21	convolutional	convolutional	ADJ
fcis-13848	61	22	neural	neural	ADJ
fcis-13848	61	23	network	network	NOUN
fcis-13848	61	24	(	(	PUNCT
fcis-13848	61	25	r	r	NOUN
fcis-13848	61	26	-	-	PUNCT
fcis-13848	61	27	cnn	cnn	PROPN
fcis-13848	61	28	)	)	PUNCT
fcis-13848	61	29	,	,	PUNCT
fcis-13848	61	30	which	which	PRON
fcis-13848	61	31	can	can	AUX
fcis-13848	61	32	accurately	accurately	ADV
fcis-13848	61	33	classify	classify	VERB
fcis-13848	61	34	arrhythmia	arrhythmia	NOUN
fcis-13848	61	35	into	into	ADP
fcis-13848	61	36	six	six	NUM
fcis-13848	61	37	categories	category	NOUN
fcis-13848	61	38	.	.	PUNCT
fcis-13848	62	1	by	by	ADP
fcis-13848	62	2	processing	process	VERB
fcis-13848	62	3	ecg	ecg	PROPN
fcis-13848	62	4	images	image	NOUN
fcis-13848	62	5	and	and	CCONJ
fcis-13848	62	6	using	use	VERB
fcis-13848	62	7	selective	selective	ADJ
fcis-13848	62	8	search	search	NOUN
fcis-13848	62	9	techniques	technique	NOUN
fcis-13848	62	10	,	,	PUNCT
fcis-13848	62	11	the	the	DET
fcis-13848	62	12	model	model	NOUN
fcis-13848	62	13	can	can	AUX
fcis-13848	62	14	identify	identify	VERB
fcis-13848	62	15	and	and	CCONJ
fcis-13848	62	16	extract	extract	VERB
fcis-13848	62	17	key	key	ADJ
fcis-13848	62	18	ecg	ecg	PROPN
fcis-13848	62	19	features	feature	NOUN
fcis-13848	62	20	.	.	PUNCT
fcis-13848	63	1	after	after	ADP
fcis-13848	63	2	training	training	NOUN
fcis-13848	63	3	and	and	CCONJ
fcis-13848	63	4	optimization	optimization	NOUN
fcis-13848	63	5	of	of	ADP
fcis-13848	63	6	the	the	DET
fcis-13848	63	7	deep	deep	ADJ
fcis-13848	63	8	neural	neural	ADJ
fcis-13848	63	9	network	network	NOUN
fcis-13848	63	10	,	,	PUNCT
fcis-13848	63	11	the	the	DET
fcis-13848	63	12	model	model	NOUN
fcis-13848	63	13	shows	show	VERB
fcis-13848	63	14	up	up	ADP
fcis-13848	63	15	to	to	ADP
fcis-13848	63	16	91.27	91.27	NUM
fcis-13848	63	17	%	%	NOUN
fcis-13848	63	18	accuracy	accuracy	NOUN
fcis-13848	63	19	and	and	CCONJ
fcis-13848	63	20	95.12	95.12	NUM
fcis-13848	63	21	%	%	NOUN
fcis-13848	63	22	f1	f1	NOUN
fcis-13848	63	23	score	score	NOUN
fcis-13848	63	24	on	on	ADP
fcis-13848	63	25	the	the	DET
fcis-13848	63	26	test	test	NOUN
fcis-13848	63	27	data	datum	NOUN
fcis-13848	63	28	set	set	VERB
fcis-13848	63	29	.	.	PUNCT
fcis-13848	64	1	these	these	DET
fcis-13848	64	2	results	result	VERB
fcis-13848	64	3	not	not	PART
fcis-13848	64	4	only	only	ADV
fcis-13848	64	5	prove	prove	VERB
fcis-13848	64	6	the	the	DET
fcis-13848	64	7	effectiveness	effectiveness	NOUN
fcis-13848	64	8	of	of	ADP
fcis-13848	64	9	deep	deep	ADJ
fcis-13848	64	10	learning	learning	NOUN
fcis-13848	64	11	technology	technology	NOUN
fcis-13848	64	12	in	in	ADP
fcis-13848	64	13	the	the	DET
fcis-13848	64	14	diagnosis	diagnosis	NOUN
fcis-13848	64	15	of	of	ADP
fcis-13848	64	16	arrhythmia	arrhythmia	NOUN
fcis-13848	64	17	,	,	PUNCT
fcis-13848	64	18	but	but	CCONJ
fcis-13848	64	19	also	also	ADV
fcis-13848	64	20	provide	provide	VERB
fcis-13848	64	21	a	a	DET
fcis-13848	64	22	more	more	ADV
fcis-13848	64	23	accurate	accurate	ADJ
fcis-13848	64	24	and	and	CCONJ
fcis-13848	64	25	timely	timely	ADJ
fcis-13848	64	26	tool	tool	NOUN
fcis-13848	64	27	for	for	ADP
fcis-13848	64	28	future	future	ADJ
fcis-13848	64	29	medical	medical	ADJ
fcis-13848	64	30	interventions	intervention	NOUN
fcis-13848	64	31	.	.	PUNCT
fcis-13848	65	1	see	see	VERB
fcis-13848	65	2	figure	figure	NOUN
fcis-13848	65	3	4	4	NUM
fcis-13848	65	4	comparison	comparison	NOUN
fcis-13848	65	5	of	of	ADP
fcis-13848	65	6	accuracy	accuracy	NOUN
fcis-13848	65	7	changes	change	NOUN
fcis-13848	65	8	and	and	CCONJ
fcis-13848	65	9	loss	loss	NOUN
fcis-13848	65	10	error	error	NOUN
fcis-13848	65	11	changes	change	NOUN
fcis-13848	65	12	in	in	ADP
fcis-13848	65	13	the	the	DET
fcis-13848	65	14	training	training	NOUN
fcis-13848	65	15	process	process	NOUN
fcis-13848	65	16	.	.	PUNCT
fcis-13848	66	1	from	from	ADP
fcis-13848	66	2	the	the	DET
fcis-13848	66	3	application	application	NOUN
fcis-13848	66	4	results	result	NOUN
fcis-13848	66	5	,	,	PUNCT
fcis-13848	66	6	the	the	DET
fcis-13848	66	7	method	method	NOUN
fcis-13848	66	8	based	base	VERB
fcis-13848	66	9	on	on	ADP
fcis-13848	66	10	regional	regional	ADJ
fcis-13848	66	11	convolutional	convolutional	ADJ
fcis-13848	66	12	neural	neural	ADJ
fcis-13848	66	13	network	network	NOUN
fcis-13848	66	14	algorithm	algorithm	NOUN
fcis-13848	66	15	is	be	AUX
fcis-13848	66	16	used	use	VERB
fcis-13848	66	17	to	to	PART
fcis-13848	66	18	identify	identify	VERB
fcis-13848	66	19	arrhythmia	arrhythmia	NOUN
fcis-13848	66	20	,	,	PUNCT
fcis-13848	66	21	and	and	CCONJ
fcis-13848	66	22	has	have	AUX
fcis-13848	66	23	achieved	achieve	VERB
fcis-13848	66	24	good	good	ADJ
fcis-13848	66	25	classification	classification	NOUN
fcis-13848	66	26	effect	effect	NOUN
fcis-13848	66	27	and	and	CCONJ
fcis-13848	66	28	accuracy	accuracy	NOUN
fcis-13848	66	29	.	.	PUNCT
fcis-13848	67	1	convolutional	convolutional	ADJ
fcis-13848	67	2	neural	neural	ADJ
fcis-13848	67	3	networks	network	NOUN
fcis-13848	67	4	are	be	AUX
fcis-13848	67	5	used	use	VERB
fcis-13848	67	6	for	for	ADP
fcis-13848	67	7	training	training	NOUN
fcis-13848	67	8	and	and	CCONJ
fcis-13848	67	9	testing	testing	NOUN
fcis-13848	67	10	,	,	PUNCT
fcis-13848	67	11	and	and	CCONJ
fcis-13848	67	12	can	can	AUX
fcis-13848	67	13	learn	learn	VERB
fcis-13848	67	14	a	a	DET
fcis-13848	67	15	large	large	ADJ
fcis-13848	67	16	number	number	NOUN
fcis-13848	67	17	of	of	ADP
fcis-13848	67	18	mapping	mapping	NOUN
fcis-13848	67	19	relationships	relationship	NOUN
fcis-13848	67	20	between	between	ADP
fcis-13848	67	21	input	input	NOUN
fcis-13848	67	22	and	and	CCONJ
fcis-13848	67	23	output	output	NOUN
fcis-13848	67	24	.	.	PUNCT
fcis-13848	68	1	there	there	PRON
fcis-13848	68	2	is	be	VERB
fcis-13848	68	3	no	no	DET
fcis-13848	68	4	pressure	pressure	NOUN
fcis-13848	68	5	for	for	ADP
fcis-13848	68	6	high	high	ADJ
fcis-13848	68	7	-	-	PUNCT
fcis-13848	68	8	dimensional	dimensional	ADJ
fcis-13848	68	9	data	datum	NOUN
fcis-13848	68	10	processing	processing	NOUN
fcis-13848	68	11	,	,	PUNCT
fcis-13848	68	12	no	no	DET
fcis-13848	68	13	need	need	NOUN
fcis-13848	68	14	to	to	PART
fcis-13848	68	15	manually	manually	ADV
fcis-13848	68	16	select	select	VERB
fcis-13848	68	17	features	feature	NOUN
fcis-13848	68	18	,	,	PUNCT
fcis-13848	68	19	and	and	CCONJ
fcis-13848	68	20	train	train	NOUN
fcis-13848	68	21	weights	weight	NOUN
fcis-13848	68	22	,	,	PUNCT
fcis-13848	68	23	so	so	SCONJ
fcis-13848	68	24	that	that	SCONJ
fcis-13848	68	25	the	the	DET
fcis-13848	68	26	feature	feature	NOUN
fcis-13848	68	27	classification	classification	NOUN
fcis-13848	68	28	effect	effect	NOUN
fcis-13848	68	29	is	be	AUX
fcis-13848	68	30	good	good	ADJ
fcis-13848	68	31	.	.	PUNCT
fcis-13848	69	1	a	a	DET
fcis-13848	69	2	deep	deep	ADJ
fcis-13848	69	3	neural	neural	ADJ
fcis-13848	69	4	network	network	NOUN
fcis-13848	69	5	model	model	NOUN
fcis-13848	69	6	was	be	AUX
fcis-13848	69	7	established	establish	VERB
fcis-13848	69	8	to	to	PART
fcis-13848	69	9	assess	assess	VERB
fcis-13848	69	10	the	the	DET
fcis-13848	69	11	risk	risk	NOUN
fcis-13848	69	12	of	of	ADP
fcis-13848	69	13	arrhythmia	arrhythmia	NOUN
fcis-13848	69	14	.	.	PUNCT
fcis-13848	70	1	the	the	DET
fcis-13848	70	2	model	model	NOUN
fcis-13848	70	3	has	have	VERB
fcis-13848	70	4	the	the	DET
fcis-13848	70	5	characteristics	characteristic	NOUN
fcis-13848	70	6	of	of	ADP
fcis-13848	70	7	clear	clear	ADJ
fcis-13848	70	8	results	result	NOUN
fcis-13848	70	9	and	and	CCONJ
fcis-13848	70	10	strong	strong	ADJ
fcis-13848	70	11	systematicness	systematicness	NOUN
fcis-13848	70	12	.	.	PUNCT
fcis-13848	71	1	it	it	PRON
fcis-13848	71	2	can	can	AUX
fcis-13848	71	3	better	well	ADV
fcis-13848	71	4	solve	solve	VERB
fcis-13848	71	5	the	the	DET
fcis-13848	71	6	problem	problem	NOUN
fcis-13848	71	7	of	of	ADP
fcis-13848	71	8	ambiguity	ambiguity	NOUN
fcis-13848	71	9	and	and	CCONJ
fcis-13848	71	10	difficult	difficult	ADJ
fcis-13848	71	11	to	to	PART
fcis-13848	71	12	quantify	quantify	VERB
fcis-13848	71	13	,	,	PUNCT
fcis-13848	71	14	and	and	CCONJ
fcis-13848	71	15	is	be	AUX
fcis-13848	71	16	suitable	suitable	ADJ
fcis-13848	71	17	for	for	ADP
fcis-13848	71	18	solving	solve	VERB
fcis-13848	71	19	various	various	ADJ
fcis-13848	71	20	non	non	ADJ
fcis-13848	71	21	-	-	ADJ
fcis-13848	71	22	deterministic	deterministic	ADJ
fcis-13848	71	23	problems	problem	NOUN
fcis-13848	71	24	.	.	PUNCT
fcis-13848	72	1	as	as	SCONJ
fcis-13848	72	2	shown	show	VERB
fcis-13848	72	3	in	in	ADP
fcis-13848	72	4	figure	figure	NOUN
fcis-13848	72	5	4	4	NUM
fcis-13848	72	6	,	,	PUNCT
fcis-13848	72	7	the	the	DET
fcis-13848	72	8	accuracy	accuracy	NOUN
fcis-13848	72	9	and	and	CCONJ
fcis-13848	72	10	loss	loss	NOUN
fcis-13848	72	11	error	error	NOUN
fcis-13848	72	12	changes	change	NOUN
fcis-13848	72	13	during	during	ADP
fcis-13848	72	14	the	the	DET
fcis-13848	72	15	training	training	NOUN
fcis-13848	72	16	process	process	NOUN
fcis-13848	72	17	are	be	AUX
fcis-13848	72	18	compared	compare	VERB
fcis-13848	72	19	.	.	PUNCT
fcis-13848	73	1	this	this	PRON
fcis-13848	73	2	further	far	ADV
fcis-13848	73	3	proves	prove	VERB
fcis-13848	73	4	the	the	DET
fcis-13848	73	5	effectiveness	effectiveness	NOUN
fcis-13848	73	6	and	and	CCONJ
fcis-13848	73	7	superiority	superiority	NOUN
fcis-13848	73	8	of	of	ADP
fcis-13848	73	9	our	our	PRON
fcis-13848	73	10	method	method	NOUN
fcis-13848	73	11	.	.	PUNCT
fcis-13848	74	1	45	45	NUM
fcis-13848	74	2	figure	figure	NOUN
fcis-13848	74	3	4	4	NUM
fcis-13848	74	4	.	.	NOUN
fcis-13848	74	5	comparison	comparison	NOUN
fcis-13848	74	6	of	of	ADP
fcis-13848	74	7	accuracy	accuracy	NOUN
fcis-13848	74	8	and	and	CCONJ
fcis-13848	74	9	loss	loss	NOUN
fcis-13848	74	10	error	error	NOUN
fcis-13848	74	11	changes	change	NOUN
fcis-13848	74	12	during	during	ADP
fcis-13848	74	13	training	training	NOUN
fcis-13848	74	14	process	process	NOUN
fcis-13848	74	15	4	4	NUM
fcis-13848	74	16	.	.	PUNCT
fcis-13848	74	17	conclusion	conclusion	NOUN
fcis-13848	74	18	in	in	ADP
fcis-13848	74	19	this	this	DET
fcis-13848	74	20	study	study	NOUN
fcis-13848	74	21	,	,	PUNCT
fcis-13848	74	22	we	we	PRON
fcis-13848	74	23	developed	develop	VERB
fcis-13848	74	24	a	a	DET
fcis-13848	74	25	region	region	NOUN
fcis-13848	74	26	-	-	PUNCT
fcis-13848	74	27	based	base	VERB
fcis-13848	74	28	convolutional	convolutional	ADJ
fcis-13848	74	29	neural	neural	ADJ
fcis-13848	74	30	network	network	NOUN
fcis-13848	74	31	(	(	PUNCT
fcis-13848	74	32	r	r	NOUN
fcis-13848	74	33	-	-	PUNCT
fcis-13848	74	34	cnn	cnn	NOUN
fcis-13848	74	35	)	)	PUNCT
fcis-13848	74	36	ecg	ecg	PROPN
fcis-13848	74	37	classification	classification	NOUN
fcis-13848	74	38	method	method	NOUN
fcis-13848	74	39	that	that	PRON
fcis-13848	74	40	can	can	AUX
fcis-13848	74	41	classify	classify	VERB
fcis-13848	74	42	arrhythmias	arrhythmia	NOUN
fcis-13848	74	43	into	into	ADP
fcis-13848	74	44	six	six	NUM
fcis-13848	74	45	categories	category	NOUN
fcis-13848	74	46	.	.	PUNCT
fcis-13848	75	1	this	this	DET
fcis-13848	75	2	method	method	NOUN
fcis-13848	75	3	first	first	ADV
fcis-13848	75	4	processes	process	VERB
fcis-13848	75	5	the	the	DET
fcis-13848	75	6	ecg	ecg	PROPN
fcis-13848	75	7	image	image	NOUN
fcis-13848	75	8	,	,	PUNCT
fcis-13848	75	9	and	and	CCONJ
fcis-13848	75	10	then	then	ADV
fcis-13848	75	11	generates	generate	VERB
fcis-13848	75	12	a	a	DET
fcis-13848	75	13	proposal	proposal	NOUN
fcis-13848	75	14	region	region	NOUN
fcis-13848	75	15	by	by	ADP
fcis-13848	75	16	selective	selective	ADJ
fcis-13848	75	17	search	search	NOUN
fcis-13848	75	18	to	to	PART
fcis-13848	75	19	accurately	accurately	ADV
fcis-13848	75	20	locate	locate	VERB
fcis-13848	75	21	a	a	DET
fcis-13848	75	22	specific	specific	ADJ
fcis-13848	75	23	region	region	NOUN
fcis-13848	75	24	of	of	ADP
fcis-13848	75	25	interest	interest	NOUN
fcis-13848	75	26	.	.	PUNCT
fcis-13848	76	1	after	after	ADP
fcis-13848	76	2	rigorous	rigorous	ADJ
fcis-13848	76	3	training	training	NOUN
fcis-13848	76	4	,	,	PUNCT
fcis-13848	76	5	this	this	DET
fcis-13848	76	6	method	method	NOUN
fcis-13848	76	7	not	not	PART
fcis-13848	76	8	only	only	ADV
fcis-13848	76	9	improves	improve	VERB
fcis-13848	76	10	the	the	DET
fcis-13848	76	11	accuracy	accuracy	NOUN
fcis-13848	76	12	and	and	CCONJ
fcis-13848	76	13	efficiency	efficiency	NOUN
fcis-13848	76	14	of	of	ADP
fcis-13848	76	15	ecg	ecg	PROPN
fcis-13848	76	16	classification	classification	NOUN
fcis-13848	76	17	,	,	PUNCT
fcis-13848	76	18	but	but	CCONJ
fcis-13848	76	19	also	also	ADV
fcis-13848	76	20	successfully	successfully	ADV
fcis-13848	76	21	establishes	establish	VERB
fcis-13848	76	22	a	a	DET
fcis-13848	76	23	deep	deep	ADJ
fcis-13848	76	24	neural	neural	ADJ
fcis-13848	76	25	network	network	NOUN
fcis-13848	76	26	model	model	NOUN
fcis-13848	76	27	,	,	PUNCT
fcis-13848	76	28	which	which	PRON
fcis-13848	76	29	shows	show	VERB
fcis-13848	76	30	high	high	ADJ
fcis-13848	76	31	accuracy	accuracy	NOUN
fcis-13848	76	32	in	in	ADP
fcis-13848	76	33	predicting	predict	VERB
fcis-13848	76	34	the	the	DET
fcis-13848	76	35	risk	risk	NOUN
fcis-13848	76	36	level	level	NOUN
fcis-13848	76	37	of	of	ADP
fcis-13848	76	38	arrhythmia	arrhythmia	NOUN
fcis-13848	76	39	.	.	PUNCT
fcis-13848	77	1	the	the	DET
fcis-13848	77	2	performance	performance	NOUN
fcis-13848	77	3	evaluation	evaluation	NOUN
fcis-13848	77	4	results	result	NOUN
fcis-13848	77	5	of	of	ADP
fcis-13848	77	6	the	the	DET
fcis-13848	77	7	test	test	NOUN
fcis-13848	77	8	data	datum	NOUN
fcis-13848	77	9	set	set	VERB
fcis-13848	77	10	show	show	VERB
fcis-13848	77	11	that	that	SCONJ
fcis-13848	77	12	the	the	DET
fcis-13848	77	13	accuracy	accuracy	NOUN
fcis-13848	77	14	rate	rate	NOUN
fcis-13848	77	15	,	,	PUNCT
fcis-13848	77	16	accuracy	accuracy	NOUN
fcis-13848	77	17	rate	rate	NOUN
fcis-13848	77	18	,	,	PUNCT
fcis-13848	77	19	recall	recall	NOUN
fcis-13848	77	20	rate	rate	NOUN
fcis-13848	77	21	and	and	CCONJ
fcis-13848	77	22	f1	f1	ADJ
fcis-13848	77	23	score	score	NOUN
fcis-13848	77	24	of	of	ADP
fcis-13848	77	25	the	the	DET
fcis-13848	77	26	model	model	NOUN
fcis-13848	77	27	have	have	AUX
fcis-13848	77	28	reached	reach	VERB
fcis-13848	77	29	a	a	DET
fcis-13848	77	30	high	high	ADJ
fcis-13848	77	31	level	level	NOUN
fcis-13848	77	32	.	.	PUNCT
fcis-13848	78	1	these	these	DET
fcis-13848	78	2	findings	finding	NOUN
fcis-13848	78	3	highlight	highlight	VERB
fcis-13848	78	4	the	the	DET
fcis-13848	78	5	great	great	ADJ
fcis-13848	78	6	potential	potential	NOUN
fcis-13848	78	7	of	of	ADP
fcis-13848	78	8	deep	deep	ADJ
fcis-13848	78	9	learning	learning	NOUN
fcis-13848	78	10	technology	technology	NOUN
fcis-13848	78	11	in	in	ADP
fcis-13848	78	12	the	the	DET
fcis-13848	78	13	diagnosis	diagnosis	NOUN
fcis-13848	78	14	of	of	ADP
fcis-13848	78	15	arrhythmia	arrhythmia	NOUN
fcis-13848	78	16	and	and	CCONJ
fcis-13848	78	17	provide	provide	VERB
fcis-13848	78	18	new	new	ADJ
fcis-13848	78	19	possibilities	possibility	NOUN
fcis-13848	78	20	for	for	ADP
fcis-13848	78	21	future	future	ADJ
fcis-13848	78	22	medical	medical	ADJ
fcis-13848	78	23	interventions	intervention	NOUN
fcis-13848	78	24	.	.	PUNCT
fcis-13848	79	1	references	reference	NOUN
fcis-13848	79	2	[	[	X
fcis-13848	79	3	1	1	NUM
fcis-13848	79	4	]	]	X
fcis-13848	79	5	smith	smith	PROPN
fcis-13848	79	6	,	,	PUNCT
fcis-13848	79	7	j.	j.	PROPN
fcis-13848	79	8	a.	a.	PROPN
fcis-13848	79	9	,	,	PUNCT
fcis-13848	79	10	&	&	CCONJ
fcis-13848	79	11	johnson	johnson	PROPN
fcis-13848	79	12	,	,	PUNCT
fcis-13848	79	13	p.	p.	PROPN
fcis-13848	79	14	r.	r.	PROPN
fcis-13848	79	15	(	(	PUNCT
fcis-13848	79	16	2019	2019	NUM
fcis-13848	79	17	)	)	PUNCT
fcis-13848	79	18	.	.	PUNCT
fcis-13848	80	1	*	*	PUNCT
fcis-13848	80	2	application	application	NOUN
fcis-13848	80	3	of	of	ADP
fcis-13848	80	4	deep	deep	ADJ
fcis-13848	80	5	learning	learning	NOUN
fcis-13848	80	6	in	in	ADP
fcis-13848	80	7	electrocardiogram	electrocardiogram	NOUN
fcis-13848	80	8	analysis	analysis	NOUN
fcis-13848	80	9	:	:	PUNCT
fcis-13848	80	10	emphasis	emphasis	NOUN
fcis-13848	80	11	on	on	ADP
fcis-13848	80	12	convolutional	convolutional	ADJ
fcis-13848	80	13	neural	neural	ADJ
fcis-13848	80	14	networks	network	NOUN
fcis-13848	80	15	*	*	PUNCT
fcis-13848	80	16	.	.	PUNCT
fcis-13848	81	1	journal	journal	PROPN
fcis-13848	81	2	of	of	ADP
fcis-13848	81	3	cardiac	cardiac	ADJ
fcis-13848	81	4	informatics	informatic	NOUN
fcis-13848	81	5	,	,	PUNCT
fcis-13848	81	6	12(3	12(3	NUM
fcis-13848	81	7	)	)	PUNCT
fcis-13848	81	8	,	,	PUNCT
fcis-13848	81	9	45	45	NUM
fcis-13848	81	10	-	-	SYM
fcis-13848	81	11	53	53	NUM
fcis-13848	81	12	.	.	PUNCT
fcis-13848	82	1	[	[	X
fcis-13848	82	2	2	2	NUM
fcis-13848	82	3	]	]	X
fcis-13848	82	4	lee	lee	PROPN
fcis-13848	82	5	,	,	PUNCT
fcis-13848	82	6	m.	m.	PROPN
fcis-13848	82	7	k.	k.	PROPN
fcis-13848	82	8	,	,	PUNCT
fcis-13848	82	9	&	&	CCONJ
fcis-13848	82	10	kim	kim	PROPN
fcis-13848	82	11	,	,	PUNCT
fcis-13848	82	12	y.	y.	PROPN
fcis-13848	82	13	h.	h.	PROPN
fcis-13848	82	14	(	(	PUNCT
fcis-13848	82	15	2018	2018	NUM
fcis-13848	82	16	)	)	PUNCT
fcis-13848	82	17	.	.	PUNCT
fcis-13848	83	1	*	*	PUNCT
fcis-13848	83	2	fusion	fusion	NOUN
fcis-13848	83	3	of	of	ADP
fcis-13848	83	4	convolutional	convolutional	ADJ
fcis-13848	83	5	neural	neural	ADJ
fcis-13848	83	6	networks	network	NOUN
fcis-13848	83	7	and	and	CCONJ
fcis-13848	83	8	recurrent	recurrent	ADJ
fcis-13848	83	9	neural	neural	ADJ
fcis-13848	83	10	networks	network	NOUN
fcis-13848	83	11	for	for	ADP
fcis-13848	83	12	ecg	ecg	PROPN
fcis-13848	83	13	classification	classification	PROPN
fcis-13848	83	14	*	*	NOUN
fcis-13848	83	15	.	.	PUNCT
fcis-13848	84	1	journal	journal	PROPN
fcis-13848	84	2	of	of	ADP
fcis-13848	84	3	biomedical	biomedical	ADJ
fcis-13848	84	4	signal	signal	NOUN
fcis-13848	84	5	processing	processing	NOUN
fcis-13848	84	6	,	,	PUNCT
fcis-13848	84	7	14(2	14(2	NUM
fcis-13848	84	8	)	)	PUNCT
fcis-13848	84	9	,	,	PUNCT
fcis-13848	84	10	110	110	NUM
fcis-13848	84	11	-	-	SYM
fcis-13848	84	12	119	119	NUM
fcis-13848	84	13	.	.	PUNCT
fcis-13848	85	1	[	[	X
fcis-13848	85	2	3	3	NUM
fcis-13848	85	3	]	]	X
fcis-13848	85	4	wang	wang	PROPN
fcis-13848	85	5	,	,	PUNCT
fcis-13848	85	6	l.	l.	PROPN
fcis-13848	85	7	,	,	PUNCT
fcis-13848	85	8	&	&	CCONJ
fcis-13848	85	9	zhang	zhang	PROPN
fcis-13848	85	10	,	,	PUNCT
fcis-13848	85	11	x.	x.	PROPN
fcis-13848	85	12	(	(	PUNCT
fcis-13848	85	13	2020	2020	NUM
fcis-13848	85	14	)	)	PUNCT
fcis-13848	85	15	.	.	PUNCT
fcis-13848	86	1	*	*	PUNCT
fcis-13848	86	2	data	datum	NOUN
fcis-13848	86	3	augmentation	augmentation	NOUN
fcis-13848	86	4	and	and	CCONJ
fcis-13848	86	5	transfer	transfer	NOUN
fcis-13848	86	6	learning	learning	NOUN
fcis-13848	86	7	in	in	ADP
fcis-13848	86	8	ecg	ecg	PROPN
fcis-13848	86	9	analysis	analysis	NOUN
fcis-13848	86	10	with	with	ADP
fcis-13848	86	11	deep	deep	ADJ
fcis-13848	86	12	neural	neural	ADJ
fcis-13848	86	13	networks	network	NOUN
fcis-13848	86	14	*	*	PUNCT
fcis-13848	86	15	.	.	PUNCT
fcis-13848	87	1	proceedings	proceeding	NOUN
fcis-13848	87	2	of	of	ADP
fcis-13848	87	3	the	the	DET
fcis-13848	87	4	international	international	ADJ
fcis-13848	87	5	conference	conference	NOUN
fcis-13848	87	6	on	on	ADP
fcis-13848	87	7	medical	medical	ADJ
fcis-13848	87	8	imaging	imaging	NOUN
fcis-13848	87	9	and	and	CCONJ
fcis-13848	87	10	informatics	informatic	NOUN
fcis-13848	87	11	,	,	PUNCT
fcis-13848	87	12	456	456	NUM
fcis-13848	87	13	-	-	SYM
fcis-13848	87	14	463	463	NUM
fcis-13848	87	15	.	.	PUNCT
fcis-13848	88	1	[	[	X
fcis-13848	88	2	4	4	NUM
fcis-13848	88	3	]	]	X
fcis-13848	88	4	patel	patel	PROPN
fcis-13848	88	5	,	,	PUNCT
fcis-13848	88	6	s.	s.	PROPN
fcis-13848	88	7	,	,	PUNCT
fcis-13848	88	8	&	&	CCONJ
fcis-13848	88	9	gupta	gupta	PROPN
fcis-13848	88	10	,	,	PUNCT
fcis-13848	88	11	a.	a.	NOUN
fcis-13848	88	12	(	(	PUNCT
fcis-13848	88	13	2021	2021	NUM
fcis-13848	88	14	)	)	PUNCT
fcis-13848	88	15	.	.	PUNCT
fcis-13848	89	1	*	*	PUNCT
fcis-13848	89	2	interpretability	interpretability	NOUN
fcis-13848	89	3	and	and	CCONJ
fcis-13848	89	4	visualization	visualization	NOUN
fcis-13848	89	5	techniques	technique	NOUN
fcis-13848	89	6	in	in	ADP
fcis-13848	89	7	deep	deep	ADJ
fcis-13848	89	8	learning	learning	NOUN
fcis-13848	89	9	for	for	ADP
fcis-13848	89	10	ecg	ecg	PROPN
fcis-13848	89	11	data	data	PROPN
fcis-13848	89	12	*	*	PROPN
fcis-13848	89	13	.	.	PUNCT
fcis-13848	89	14	journal	journal	PROPN
fcis-13848	89	15	of	of	ADP
fcis-13848	89	16	medical	medical	ADJ
fcis-13848	89	17	systems	system	NOUN
fcis-13848	89	18	and	and	CCONJ
fcis-13848	89	19	technologies	technology	NOUN
fcis-13848	89	20	,	,	PUNCT
fcis-13848	89	21	15(1	15(1	NUM
fcis-13848	89	22	)	)	PUNCT
fcis-13848	89	23	,	,	PUNCT
fcis-13848	89	24	25	25	NUM
fcis-13848	89	25	-	-	SYM
fcis-13848	89	26	32	32	NUM
fcis-13848	89	27	.	.	PUNCT
fcis-13848	90	1	[	[	X
fcis-13848	90	2	5	5	NUM
fcis-13848	90	3	]	]	X
fcis-13848	90	4	fernandez	fernandez	PROPN
fcis-13848	90	5	,	,	PUNCT
fcis-13848	90	6	r.	r.	PROPN
fcis-13848	90	7	,	,	PUNCT
fcis-13848	90	8	&	&	CCONJ
fcis-13848	90	9	lopez	lopez	PROPN
fcis-13848	90	10	,	,	PUNCT
fcis-13848	90	11	v.	v.	PROPN
fcis-13848	90	12	(	(	PUNCT
fcis-13848	90	13	2020	2020	NUM
fcis-13848	90	14	)	)	PUNCT
fcis-13848	90	15	.	.	PUNCT
fcis-13848	91	1	*	*	PUNCT
fcis-13848	91	2	real	real	ADJ
fcis-13848	91	3	-	-	PUNCT
fcis-13848	91	4	time	time	NOUN
fcis-13848	91	5	arrhythmia	arrhythmia	NOUN
fcis-13848	91	6	detection	detection	NOUN
fcis-13848	91	7	with	with	ADP
fcis-13848	91	8	wearable	wearable	ADJ
fcis-13848	91	9	devices	device	NOUN
fcis-13848	91	10	:	:	PUNCT
fcis-13848	91	11	emphasis	emphasis	NOUN
fcis-13848	91	12	on	on	ADP
fcis-13848	91	13	edge	edge	NOUN
fcis-13848	91	14	computing	compute	VERB
fcis-13848	91	15	with	with	ADP
fcis-13848	91	16	deep	deep	ADJ
fcis-13848	91	17	learning	learning	NOUN
fcis-13848	91	18	models	model	NOUN
fcis-13848	91	19	*	*	NOUN
fcis-13848	91	20	.	.	PUNCT
fcis-13848	92	1	journal	journal	PROPN
fcis-13848	92	2	of	of	ADP
fcis-13848	92	3	mobile	mobile	ADJ
fcis-13848	92	4	health	health	NOUN
fcis-13848	92	5	,	,	PUNCT
fcis-13848	92	6	7(4	7(4	NUM
fcis-13848	92	7	)	)	PUNCT
fcis-13848	92	8	,	,	PUNCT
fcis-13848	92	9	210	210	NUM
fcis-13848	92	10	-	-	SYM
fcis-13848	92	11	218	218	NUM
fcis-13848	92	12	.	.	PUNCT