Interictal Epileptiform Discharge Detection Using Multi-Head Deep Convolutional Neural Network.

Abstract

Interictal epileptiform discharges (IEDs) are intermittent electrophysiological events that occur in patients with epilepsy between seizures. Automated detection of IEDs helps clinician to identify cortical irritations and relations to seizure recurrence. It also reduces the necessity of visual inspection by physicians interpreting the EEG. This paper presents a novel deep learning-based approach that combines one-dimensional local binary pattern symbolization method with a regularized multi-head one-dimensional convolutional neural network to learn unique morphological patterns from different EEG sub-bands for IED detection. Experimentation using the Temple University Events corpus scalp EEG data shows promising performance, e.g. F1-score of 87.18%.