Prediction of Seizure in the EEG Signal with Time Aware Recurrent Neural Network

Abstract

The human brain's actions are reflected by the significant physiological data relying on the Electroencephalogram (EEG), which is utilized in the detection of epileptic seizures and the diagnosis of epilepsy. The visual inspection process of a vast quantity of EEG data by any human expert is time-consuming and the judgemental process may vary or be inconsistent among the physician. Hence, an automated process in detection and diagnosis is initiated by utilizing deep learning approaches. The Convolutional Neural Network (CNN) is incorporated to correctly recognize the irregular inter-ictal discharges as non-seizures, but could not detect the ictal state and slower oscillations. To improve the performance of CNN for detecting seizures' ictal state and slower oscillations, Recurrent Neural Network (RNN) is combined with the CNN model. An RNN evokes every processed information via time and it assists in the prediction of time series data. The processed feature in RNN remembers the preceding input information which is Long Short Term Memory (LSTM). The investigational outcome of the proposed Time Aware CNN and Recurrent Neural Network (TA-CNN-RNN) attained effective classification accuracy. The experiments analysis exhibits that the TA-CNN-RNN achieves an accuracy of 89%, 88.6%, and 88.7% on CHB-MIT-EEG, Bonn-iEEG, and VIRGO-EEG databases, respectively compared to the Entropy+LSSVM, LBP+KNN and P-one-class SVM methods for epilepsy detection.