Deep Learning Classification of Epileptic Magnetoencephalogram
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Keywords
Magnetoencephalography, Epilepsy, Deep learning signal classification, Artificial Neural Networks, Feed-Forward Neural Networks, Convolutional Neural Networks, Inception V3
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 effectiveness. 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.
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