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Conference Paper

Deep Learning Classification of Epileptic 
Magnetoencephalogram 

Andreas Stylianou1, Lefteris Koumakis2, Maria Hadjinicolaou3, Adam Adamopoulos1,* and Alkinoos Athanasiou4

1 Medical Physics Laboratory, Department of Medicine, Democritus University of Thrace, Alexandroupolis, Greece.
2 Foundation for Research and Technology—Hellas (FORTH), Institute of Computer Science, Bioinformatics Laboratory, Science and 
Technology Park of Crete, Greece.
3 School of Science and Technology, Hellenic Open University, Patras, Greece.
4 Lab of Medical Physics, School of Medicine, Aristotle University of Thessaloniki, Greece.

* Corresponding Author Email: adam@med.duth.gr 

ABSTRACT

In this research, we study several statistical methods for feature extraction from Magnetoencephalography (MEG) Signals and 
classification of these signals into two classes: epileptic and healthy, based on the extracted features. We, then, apply automated 
feature extraction techniques by means of deep learning using several Artificial Neural Network (ANN) models. Our goal is to 
try various methods and models for MEG Signal classification and draw some conclusions about their functionality and effec-
tiveness. We base our study on our theoretical knowledge of the neurology of epilepsy, previous studies of epileptic seizure 
imaging and recognition using MEG and Electroencephalogram (EEG) as well as the Signal Processing Theory and techniques. 
We apply several advanced classification methods with the use of ANN like Feed-Forward ANN, Convolutional Neural Networks 
(Convolutional NN), and Inception V3. The results of this study are very encouraging and can be a base for future research on 
the subject of epileptic seizure recognition, prediction, and prevention. 

Keywords—Magnetoencephalography, Epilepsy, Deep learning signal classification, Artificial Neural Networks, Feed-
Forward Neural Networks, Convolutional Neural Networks, Inception V3. 

Copyright © 2024. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY): Creative Commons - 
Attribution 4.0 International - CC BY 4.0. The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright 
owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduc-
tion is permitted which does not comply with these terms.

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J Global Clinical Engineering Vol.6 Special Issue 6: 2024 90

INTRODUCTION

Epilepsy is one of the most common neurological 
disorders with tens of millions of patients all over the 
world. Epileptic patients suffer from seizures which are 
the most important symptom. A patient is said to suffer 
from epilepsy after two or more unprovoked seizures 
separated by at least 24 hours.1 Seizures vary in type, dura-
tion, and severity, but in any case, they are an unpleasant, 
even painful experience for the patient. Ranging from a 
short lack of consciousness to strong muscular spasms, 
seizures may also pose a significant danger to the patient. 
Injuries may be caused by falling to the ground or while 
handling dangerous objects or machinery. The abnor-
mal firing of neurons (up to 500 times per second) may 
also damage the brain cells, especially during prolonged 
seizures or ones that appear in succession (status epi-
lepticus).2 A number of previous works3 stress the need 
for implementing automated methods for the detection 
of epileptic activity as well as automated diagnosis and 
automatic prediction of epileptic seizures. Such meth-
ods should help reduce human errors by specialized 
personnel due to the fatigue after long hours of tracing 
tiny differences among dozens of recorded MEG images, 
playing a critical role in preventing epileptic seizures or 
enabling continuous machine monitoring of epileptic 
patients in critical condition. Several advanced models 
regarding EEG classification have been published previ-
ously.3 Significantly fewer publications address the issue 
of MEG classification using mostly advanced Artificial 
Neural Networks (ANN)3 but none of them thoroughly 
examine basic ANN models. Therefore, our target was to 
test, compare, and evaluate some basic models to build 
a solid understanding of the characteristics of our data 
and gain insights into the model’s behavior. This would 
serve as a preliminary study leading to a larger project 
to investigate more sophisticated, optimized, fine-tuned 
models. The results of our study are very encouraging 
and can constitute a basis for future research on epileptic 
seizure recognition, prediction, and prevention.

METHODS 

Magnetoencephalography is a neuroimaging technique 
that utilizes an array of sensors placed slightly above the 
scalp. The use of Superconducting Quantum Interference 

Devices (SQUID) makes MEG very sensitive to the micro-
scopic alterations of the magnetic field produced by brain 
cell electrical activity. Thus, it achieves a very good spatial 
resolution (up to 5 mm) as well as a great time resolu-
tion, at the scale of one millisecond or even better, which 
makes MEG a great tool for tracing real-time changes in 
brain activity and state. It can be used in combination 
with other imaging techniques (MRI, fMRI, PET, PET-CT) 
to give a detailed 3D imaging of brain activity in specific 
areas. It is non-invasive and it is completely safe, causing 
no discomfort. Moreover, it can detect epileptic activity 
and spot epileptic foci in the normal brain activity of the 
patient, without inducing unpleasant and even painful 
seizures to the patient.4

Epileptic activity appears in EEG and MEG as irregular 
patterns, in the form of spikes, spikes-and-slow waves, 
or sharp waves (Figure 1). The morphology of spikes and 
sharp waves in EEG was thoroughly analyzed by Gortman 
and these waves can be used for epilepsy diagnosis.5 
Although studies are being carried out6, there still is no 
formal definition of epileptic spikes in MEG. However, 
even if it seems an oxymoron, compared to EEG signals, 
“MEG spike yield and localization are superior to EEG”.7 
Epileptic signals in MEG have different morphological 
characteristics (duration, shape, and sharpness) from 
those in EEG. This can be explained by the small affection 
on the MEG signal from the interference from the skull and 
scalp. Furthermore, muscular activity and eye movement 
cause much less effect on MEG.8 We should note that there 
is far less research that applies Deep Learning Models to 
MEG than to EEG. SQUID is a very expensive device with 
an even more expensive installation requiring a Faraday 
cage to isolate the super-sensitive SQUID from magnetic 
interference. This raises the total cost to a few million 
Euros. The MEG signals used in our study were recorded 
in the MEG Unit of the Laboratory of Medical Physics, De-
partment of Medicine, Democritus University of Thrace, 
placed at Alexandroupolis, Greece, from patients who had 
been diagnosed with epilepsy by specialized neurologists 
and were referred for MEG evaluation. MEG signals were 
recorded with patients in a rest state and with eyes closed. 
In the present study, we worked on MEG signals recorded 
from 122 points of the patients’ brains, with a sampling 
frequency of 256 Hz and 9 sec duration. A Low-Pass Fil-
ter with a cutoff frequency of 30 Hz was applied on all 

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91 J Global Clinical Engineering Vol.6 Special Issue 6: 2024

channels. Some channels contain out-of-limit values due 
to noise and artifacts and are thus rejected. The remaining 
signals were segmented into 5436 items of 1 channel—1 
sec each, therefore each item contains 256 samples. Each 
item is classified individually by specialized neurologists 
as a signal-carrying epileptic activity (2059 items) or not 
(3377 items). Despite the absence of healthy patients, we 
have an adequate number of non-epileptic items in our 
dataset (62.1%). Our models were built and tested on a 
Hewlett-Packard Elite 800 G9 machine with an Intel i5 
10500 3.1 GHz 6-core processor and 16 GB RAM, using 
MATLAB R2018a as the programming environment. In 
this study, we test a simple 3 and 4-layer CNN on colored 
images representing the heatmap of the signal spectrum 
as well as on black-and-white images of the signal. We 
also test a partially pre-trained Inception V3 on colored 
images of the signal spectrum. We then compare the re-
sults to those of a simple Feed-Forward Neural Networks 
(FFNN) with 3 and 4 hidden layers applied to the signal 
values of the same signals.

 Since Convolutional Neural Network Models are es-
pecially effective in image classification, we wanted to 
test the two models (3-4 layer CNN and Inception V3) 
on images of the MEG signal and images of the spectrum 
of the same signal. Similar work was carried out giving 
impressive results with images obtained from EEG.9,10 We 
use a black-and-white bitmap image file, 256 × 256 pixels, 
to create images from the signal segments, 256 samples 
or 1 second long. Samples will appear as white dots on a 
black background and the displacement of dots from the 
middle of the image will be proportional to the value of the 
corresponding sample (Figure 2). To create images from 
the signal spectrum we apply Short-Time Fourier Trans-
form on the signal with a shifting window 128 samples 

wide producing a heatmap-like RGB image, 30 × 32 pixels, 
and 3 color channels (Figure 3). Convolutional Neural 
Network is a Deep Learning model for processing data 
with grid pattern-like images. It’s inspired by the optical 
cortex of animals, and it’s designed to automatically learn 
and adapt to spatial feature hierarchies, beginning from 
low and moving towards higher-level patterns. Typically, 
it consists of three types of layers: convolution layer, pool-
ing layer, and fully connected layer. The first two operate 
as feature extractors while the third maps the extracted 
features to the final output, performing classification. The 
role of the convolution layer is fundamental. Pixel values 
in digital images are stored in a two-dimensional grid, a 
matrix. An optimized feature extractor, called the kernel, is 
applied to each position of the image. Every layer’s output 
is the input of the next layer, so the extracted features may 
progressively become more complex. The parameters of 
the kernels are optimized by training performed using 
the backpropagation algorithm—gradient descent.11 For 
processing the black and white 256 × 256 signal images, 
we create a CNN of 3 convolutional layers. For processing 
the RGB 30 × 32 spectrum images, we create a CNN of 
4 convolutional layers. Inception V3 is a Deep Learning 
model based on Convolutional Networks, used in Image 
Classification. It is an improved version of Inception V1, 
published as GoogLeNet in 2014, with 4 major modifica-
tions: Factorization into Smaller Convolutions, Spatial 
Factorization into Asymmetric Convolutions, Utility of 
Auxiliary Classifiers, and Efficient Grid Size Reduction12, 
which was developed by a Google Team. Inception V3 is 
made of 42 layers, a few more than in V1 and V2. However, 
the effectiveness of the model is impressively boosted. As 
expected, Inception V3 has greater accuracy and a smaller 
computational cost compared to the previous versions. 
It even has lower error rates compared to previous and 
newer image classification models.13 We download the 
pre-trained Inception V3 Network with all the necessary 
libraries and data from the MATLAB command line. In-
ception V3 is pre-trained on more than a million images 
of the ImageNet database. It has 316 layers in total and 
can classify images into 1000 object categories.14 Unfor-
tunately, we lack the computational power, memory, and 
time to perform full training on our own dataset. Instead, 
we keep the first 198 layers frozen by using the Freez-
eWeights function, leaving the other layers’ parameters 

FIGURE 1. Spikes, Spike-and-slow waves, and Sharp waves.

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free to adapt during the training. We estimate that the 
first layers of the network form the simple, low-level 
patterns common to all kinds of images. The higher-level 
patterns built in the last levels, on the other hand, are 
more important and are the ones that give the differ-
ences in the images, so they need to be formed freely 
during the training. For better visibility of our images 
by the network, we augment the pixel range by using 
the imageDataAugmenter tool. Last, but not least, we 
conducted the simplest experiment of all. We fed our raw 
data in a relatively small FFNN—three hidden layers of 
sizes 64, 32, and 16. The Levenberg-Marqurdt algorithm 
is used to train the network and we use Mean Squared 
Error as the error measure. Each input item consists of 
256 numerical sample values of our signal, one second 
long (1s). We based this last experiment on the estimate 
that the Neural Network has the flexibility to extract the 
correct relations by calculating the appropriate weights 
that minimize the error. Also, we know that the sample 
values contain all the information in the signal.

RESULTS 
We split our data into train and test-set randomly, 70% 

train-set and 30% test-set. All our models automatically 
use an independent part of the train set for validation. 
For the evaluation of our results, we focus mainly on the 
metric of accuracy (sensitivity and specificity were also 
measured). Accuracy is a qualitative metric of performance 
that gives the proximity of a measurement to the actual 
value.15 We rely on this metric because our study consists 
of binary classification where the numbers of items of 
the two classes do not differ significantly. Also, accuracy 
includes other metrics (precision and trueness) and is 
a function of these metrics. We performed the training 
process of a three-layer CNN several times. In the best 
case, we have 85% accuracy while the average accuracy 
is 78.22% after approximately 7 minutes of training. 
Although far from perfect, these results are surprisingly 
good. Nobody expected a simple three-layer CNN could 
trace patterns and relations among them in a black pic-
ture with 256 small white dots. These results must be 
further studied and may help create optimized hybrid 
MEG Classification models. We follow the same procedure 

FIGURE 2. Color bitmap image representing the item’s spec-
tral heatmap.

FIGURE 3. Black-and-white bitmap image produced from the 
item’s signal.

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93 J Global Clinical Engineering Vol.6 Special Issue 6: 2024

with the spectra images. This time, all experiments 
gave us very good results, averaging 90.15% accuracy 
(91.40% in the best case) within just a few seconds. We 
train the Inception V3 network with a maximum epoch 
number 6 and an initial learning rate of 10−4. The train-
ing process lasts more than 50 hours. Unfortunately, 
this time the results are not so encouraging, considering 
the magnitude and complexity of the network as well 
as the time and effort spent. The prediction accuracy is 
only 74% and we can observe that only the first train-
ing epoch raises the accuracy slightly higher than 70%, 
while further improvement is very slow. The cause of 
this poor performance lies in the fact that the network 
was trained on an enormous set of common images that 
vastly differ from MEG images, and thus, even partially, 
creating low-level patterns that are useless in our case. 
Unfortunately, our resources and time did not allow us 
to design improved experiments with the Inception V3 
network. The accuracy achieved by the FFNN when val-
ues of the signal samples are fed directly to it, exceeded 
all expectations, since it is, by far, the best result in all 
our experiments. In our third effort, after 47 minutes of 
training, we got a prediction with 95.4% accuracy which 
is impressive, considering the simplicity of the model and 
data. Driven by these encouraging results, we conducted 
a final experiment adding another layer of 32 neurons 
before the last 16-neuron layer. After approximately 2 
hours of training, we obtain a trained network capable 
of slightly more accurate classification capability (96.2% 
accuracy). Tables 1 and 2 present the comparative results 

given by the three methods of classification we tested 
in our experiments.

DISCUSSION 
The obtained results, summarized in Table 1 for CNN 

and Inception V3 and in Table 2 for FFNN, are very promis-
ing since the FFNN on the signal values achieves impres-
sive results (Accuracy = 96.2%) despite the simplicity 
of the model. Also, the CNN on the spectrum heatmap 
images shows good results with accuracy reaching 90%. 
These results underline the significance of the MEG as 
a powerful tool for obtaining high-resolution and high 
signal-to-noise ratio brain signals. The epileptic spikes 
appear sharper and are easier to observe in MEG than 
in EEG. The fact that epileptic spikes are more clearly 
observed in MEG than in EEG can be verified by the good 
results (Accuracy = 96.2%) of a simple FFNN. Even less 
sophisticated models perform well, showing the power of 
MEG as a diagnostic tool for epilepsy. A basic FFNN with 
3 hidden layers is capable of successfully classifying MEG 
signals with an accuracy of up to 95.4% and a slightly 
larger FFNN with 4 layers can classify MEG Signals with 
96.2% accuracy. A three-layer FFNN is equivalent to the 
extraction of third-order statistics from our data. The 256 
raw sample values of our signal contain all the available 
information in the signal. Our three-layer network shows 
the ability to mine the information hidden in these values 
by creating the correct weighted combination of signal 
sample values. It is obvious that this simple model needs 
further investigation and experimentation. Unfortunately, 
our dataset lacks recordings of healthy subjects. It is 

TABLE 1. CNN and Inception V3 results (accuracy %).

Experiment CNN on Signal CNN on Spectrum Inception V3

1 85.00 90.50 73.90

2 77.30 89.70

3 82.30 90.60

4 62.20 89.10

5 84.30 89.60

6 78.22 91.40

Average 78.22 90.15 73.90

TABLE 2. FFNN results (accuracy % and network structure).

Experiment FFNN on signal

1 87.60  [64 32 16]

2 88.50  [64 32 16]

3 95.40  [64 32 16]

4 90.90  [64 32 16]

5      96.20  [64 32 32 16]

Average      91.72

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essential to test our models on such a dataset. We need 
to test whether the binary classification models will suf-
fice, considering the variability of the signals. Such a case 
might require more than binary classification (more than 
two classes). Although not as impressive, we also have 
a satisfactory performance of the CNN on the Spectra 
images, achieving 91.4% accuracy. It is observed that 
signals with epileptic activity contain high-frequency 
components, so we should try running experiments us-
ing a Low-Pass Filter with a higher cut-off frequency to 
avoid losing this information. Conclusions Based on the 
results and the discussion above, we need to focus on the 
following points in our future work: Obtain MEG record-
ings using a higher sampling rate and higher threshold 
on the Low-Pass Filter, more sophisticated filtering of the 
artifacts and noise, so that gain more information that lies 
within epileptic signals and an even clearer distinction 
to the non-epileptiform activity. Also, the inclusion of 
recordings from healthy subjects in the datasets is neces-
sary to create more reliable models. Although we do not 
believe this would change the models’ performance, since 
there are no serious deviations in the values of sensitivity 
and specificity, it is necessary to verify our models on a 
dataset nearer to real-world conditions, where epileptic 
signals are significantly fewer. Also, we should try to 
improve the quality of the images fed to CNN. A different 
colormap and a better analysis could potentially improve 
the performance of the CNN on the spectra image clas-
sification. Also, we need to try a different representation 
for the signal images so that the signal curve is clearly 
depicted. An Inception V3 Network with no previous 
training could prove more effective in such a case. If the 
untrained Inception V3 performs significantly better than 
the FFNN, we then can try to reduce the training time and 
cost by introducing some pre-trained layers. Area Under 
Curve (AUC) metric should be used for model validation, 
a step we omitted in our research due to the lack of time 
and resources. Metaheuristic searching algorithms, like 
Genetic Algorithms, should be used for FFNN structure 

optimization. Finally, we plan to develop an optimized 
hybrid NN Model by combining elements and layers from 
the best-performing basic models.

CONCLUSION 
Based on the results and the discussion above, we 

need to focus on the following points in our future work: 
Obtain MEG recordings using a higher sampling rate and 
higher threshold on the Low-Pass Filter, more sophisti-
cated filtering of the artifacts and noise, so that gain more 
information that lies within epileptic signals and an even 
clearer distinction to the non-epileptiform activity. Also, 
the inclusion of recordings from healthy subjects in the 
datasets is necessary to create more reliable models. Al-
though we do not believe this would change the models’ 
performance, since there are no serious deviations in the 
values of sensitivity and specificity, it is necessary to verify 
our models on a dataset nearer to real-world conditions, 
where epileptic signals are significantly fewer. Also, we 
should try to improve the quality of the images fed to 
CNN. A different colormap and a better analysis could 
potentially improve the performance of the CNN on the 
spectra image classification. Also, we need to try a differ-
ent representation for the signal images so that the signal 
curve is clearly depicted. An Inception V3 Network with 
no previous training could prove more effective in such a 
case. If the untrained Inception V3 performs significantly 
better than the FFNN, we then can try to reduce the 
training time and cost by introducing some pre-trained 
layers. Area Under Curve (AUC) metric should be used for 
model validation, a step we omitted in our research due 
to the lack of time and resources. Metaheuristic searching 
algorithms, like Genetic Algorithms, should be used for 
FFNN structure optimization. Finally, we plan to develop 
an optimized hybrid NN Model by combining elements 
and layers from the best-performing basic models.

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95 J Global Clinical Engineering Vol.6 Special Issue 6: 2024

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	Niki Pandria*, Anastasia Barboudi, Vasileia Petronikolou, Panagiotis Antoniou and Panagiotis D. Bamidis

	Novel Functional Electrical Stimulation Parameter Optimization for Neurorehabilitation Using Both Conventional and AI Techniques 
	Arsenios Arsenidis1, Alexandros Moraitopoulos2, Alkinoos Athanasiou2, Alexandros Vildiridis3, Panagiotis Bamidis2, Petros Stefaneas4 and Alexandros Astaras5 

	Leveraging Web Scraping and API Integration for Efficient Medical Device Data Management 
	Agapi Konstantina Liontou1,*, Spilios Zisimopoulos2 and Aris Dermitzakis1 

	Human Muscle State Machine Using Electromyography Classification with Machine Learning 
	George Lyssas1,*, Konstantinos Mitsopoulos1, Dimitris Zantzas2, Anestis Kalfas2, Panagiotis D. Bamidis1 

	Kinematic and Dynamic Analysis of Lower Limb Movement: Towards the Design of a Wearable Rehabilitation Assistant Device 
	Filippos Margaritis1,*, Konstantinos Mitsopoulos1, Kostas Nizamis2, Alkinoos Athanasiou1 and Panagiotis D. Bamidis1 

	A Novel Dermatological Diagnosis Support Device Based on Electrical Impedance Spectroscopy 
	Alexandros Moraitopoulos1,*, Konstantinos Mitsopoulos1, Christina Kemanetzi2, Panagiotis Bamidis1 and Alexandros Astaras3

	Software Skills Identification: A Multi-Class Classification on Source Code Using Machine Learning
	Dimitris Bamidis, Ilias Kalouptsoglou, Apostolos Ampatzoglou, Alexandros Chatzigeorgiou*

	Improvement of Aortic Valve Stenosis Classification in Patients Through Computational Fluid Dynamics Model
	Ioannis Makropoulos, Dimitris Zantzas, Vasilis Gkoutzamanis, Anestis Kalfas*

	Kinematic and Dynamic Analysis of the Human Hand’s Articulation for Wearable Soft-Robotic Device Applications
	Paschalina-Danai Sarra, Vasiliki Fiska, Konstantinos Mitsopoulos, Diamanto Mylopoulou, and Panagiotis D. Bamidis*

	Deep Learning Classification of Epileptic Magnetoencephalogram 
	Andreas Stylianou1, Lefteris Koumakis2, Maria Hadjinicolaou3, Adam Adamopoulos1,* and Alkinoos Athanasiou4


