Automatic Epileptic Seizure Onset-Offset Detection Based On CNN in Scalp EEG

Abstract

We establish a deep learning-based method to automatically detect the epileptic seizure onsets and offsets in multi-channel electroencephalography (EEG) signals. A convolutional neural network (CNN) is designed to identify occurrences of seizures in EEG epochs from the EEG signals and an onset-offset detector is proposed to determine the seizure onsets and offsets. The EEG signals are considered as inputs and the outputs are the onset and offset. In the CNN, a filter is factorized to separately capture temporal and spatial patterns in EEG epochs. Moreover, we develop an onset-offset detection method based on clinical decision criteria. As a result, verified on the whole CHB-MIT Scalp EEG database, the CNN model correctly detected seizure activities over 90%. Furthermore, combined with the onset-offset detector, this method accomplished F1 of 64.40% and essentially determined the seizure onset and offset with absolute onset and offset latencies of 5.83 and 10.12 seconds, respectively.