Epileptic Classification With Deep-Transfer-Learning-Based Feature Fusion Algorithm

Epilepsy ictal detection based on scalp electroencephalograms (EEGs) has been comprehensively studied in the past decades. But few attentions have been paid to the preictal classification. In this paper, a comprehensive study on epileptic state classification based on deep transfer learning (TL) is presented. The main contributions include: 1) the subband mean amplitude spectrum (MAS) map that characterizes the typical rhythms of brain activities is extracted for EEG representation, 2) 5 representative deep neural networks (DNNs) pre-trained on ImageNet are applied for EEG feature TL, 3) a 7-layer hierarchical neural network (HNN) that consists of 3 fully-connected (Fc) and 3 dropout layers followed by a softmax layer is developed to perform the epileptic state probability learning and classification. Experiments on the benchmark CHB-MIT and iNeuro EEG databases that contain several different types of seizures show that the proposed algorithm achieves the highest overall accuracies of 96.97% and 87.87% on the 5-state epileptic classification, respectively, that outperforms many existing state-of-the-art methods presented in the paper.