Epilepsy Detection Method Based on the Time-gated Feature Network

Abstract

Epilepsy is a nervous system disease, which is caused by abnormal discharge of brain neurons. The clinical manifestations are generalized seizures, clonus, loss of consciousness, and shock. An electroencephalogram (EEG) can accurately capture the changes in EEG activities. Therefore, EEG signals are used to detect seizures. In this paper, an epilepsy detection model based on a time-gated feature network (TFGN) is proposed. Firstly, the original EEG signal is preprocessed, and the preprocessed signal is sent into the TFGN detection model which integrates feature extraction, feature selection, and classification to obtain the detection results of epilepsy. Through the verification of data from different ages and channels, the detection accuracy of the TFGN detection model is higher than that of the traditional detection model, and the validity and comprehensiveness of the TFGN detection model are verified.