The Deep Learning Based Epileptic Seizure Detection Using 2-layer Convolutional Network with Long Short-Term Memory

Abstract

Epilepsy is a pervasive chronic neurological disorder characterized through irregular electrical discharges in the brain which causes seizures. Epilepsy seizure is a disorder that affects the brain cells with an influence on an effectiveness of central nervous system. Electroencephalography (EEG) is a majorly utilized method for epileptic seizure detection and diagnosis. In this research, Deep Learning (DL) methods of 2-layer Convolutional Neural Network (CNN) with Long Short-Term Memory (LSTM) are proposed for an automatic detection and diagnosis of an epileptic seizure. In the pre-processing phase, a Butterworth filter method of order 2 is used to remove noise in the EEG signal. The 2-layer CNN is used for the process of feature extraction. In 2-layer LSTM, one layer is utilized to perform short-term dependencies, while another layer is utilized to perform long term dependencies. In the end, the proposed method classifies seizures into epileptic and non-epileptic. The results demonsrates that the proposed method delivers performance metrics of better accuracy of 99.90% and sensitivity of 90.06% using CHB-MIT and Bonn datasets which contains EEG signals as compared to the existing methods like CNN and Epilepsy-Net.