Studying Multi-Frequency Multilayer Brain Network via Deep Learning for EEG-Based Epilepsy Detection

Abstract

Epilepsy makes the patients suffer great pain and has a very bad impact on daily life. In this paper, a novel method is proposed to implement electroencephalogram (EEG)-based epilepsy detection, in which multi-frequency multilayer brain network and deep learning are jointly utilized. Firstly, based on the multi-frequency characteristics of brain, we construct a multilayer brain network from the multi-channel EEG signals. The time, frequency and channel-related information from EEG signals are all mapped into the multilayer network topology, making it an effective feature for epilepsy detection. Subsequently, with multilayer brain network as input, a multilayer deep convolutional neural network (MDCNN) model is designed. MDCNN model has two blocks and uses a parallel multi-branch design in the first block, which exactly matches the multilayer structure of the proposed brain network. The experimental results on publicly available CHB-MIT datasets show that the proposed method can accurately detect epilepsy, with a high average accuracy of 99.56%, sensitivity of 99.29%, and specificity of 99.84%. All these provide an efficient solution for characterizing the complex brain states using multi-channel EEG signals.