Epileptic Seizure Prediction Based on Convolutional Recurrent Neural Network with Multi-Timescale

Abstract

Epilepsy is a common disease that is caused by abnormal discharge of neurons in the brain. The most existing methods for seizure prediction rely on multi kinds of features. To discriminate pre-ictal from inter-ictal patterns of EEG signals, a convolutional recurrent neural network with multi-timescale (MT-CRNN) is proposed for seizure prediction. The network model is built to complement the patient-specific seizure prediction approaches. We firstly calculate the correlation coefficients in eight frequency bands from segmented EEG to highlight the key bands among different people. Then CNN is used to extract features and reduce the data dimension, and the output of CNN acts as input of RNN to learn the implicit relationship of the time series. Furthermore, considering that EEG in different time scales reflect neuron activity in distinct scope, we combine three timescale segments of 1 s, 2 s and 3 s. Experiments are done to validate the performance of the proposed model on the dataset of CHB-MIT, and a promising result of 94.8% accuracy, 91.7% sensitivity, and 97.7% specificity are achieved.