Epileptic Seizure Detection Using Deep Convolutional Network

Abstract

In this paper, a patient specific seizure detection system using channel-restricted convolutional neural network(CR-CNN) with deep structure is represented. The binary patterns of brainwave activity reflected on ictal and interictal EEG are auto-memorized based on back-propagation mechanism. It is well trained using massive historical scalp EEG data of 23 pediatric patients with epilepsy from CHB-MIT database. Experimental results demonstrate that the proposed detector achieves the state of the art performance. The average false alarms rate reaches 0.12 per hour and only one out of the 167 seizures is missed.