EEG Signal Epilepsy Detection System Based on Convolutional Neural Network and Memristor Array

Epilepsy is one of the most harmful diseases to human society. EEG(Electroencephalogram, EEG) can be used to detect the onset of epilepsy in a timely and effective manner. In this work, a detecting system based on convolutional neural network is designed. The system consists signal acquisition module, pre-processing module and neuromophic calculation module, and is capable of detecting epilepsy activity from EEG signal, the neuromophic module is designed based on memristor array. The system is simulated and tested on a 480-subject database corresponding to the 8 most significant channels from each patient for a total of 64 EEG signal channels, and have obtained the detecting accuracy of 98.46%, which proved its excellent performance on epilepsy detecting.