Epileptic State Classification by Fusing Hand-Crafted and Deep Learning EEG Features

Abstract

Seizure onset detection and epileptic preictal prediction based on electroencephalogram (EEG) signals have been a challenge problem in the research community. In this brief, a novel epileptic states classification algorithm based on the multichannel EEGs representation using multiple hand-crafted features, the feature fusion and transfer learning (TL) with multiple pre-trained deep neural networks (DNNs), the discriminative feature extraction and epileptic state classification with a hierarchical neural network (HNN), is developed. The mean amplitude spectrum (MAS), mean power spectral density (MPSD) and wavelet packet features (WPFs) are firstly derived and fused into an image feature for multichannel EEGs representation. Then, 5 classical pre-trained DNNs are directly adopted as feature extractors on the fused image feature. A 7-layer fully-connected (FC) HNN is finally constructed for discriminative feature learning and epileptic state classification. The effectiveness is demonstrated through experiments on the CHB-MIT and the iNeuro epilepsy EEG databases.