Classification of Epileptic EEG Signals with Stacked Sparse Autoencoder Based on Deep Learning

Abstract

Automatic detection of epileptic seizure plays an important role in the diagnosis of epilepsy for it can obtain invisible information of epileptic electroencephalogram (EEG) signals exactly and reduce the heavy burdens of doctors efficiently. Current automatic detection technologies are almost shallow learning models that are insufficient to learn the complex and non-stationary epileptic EEG signals. Moreover, most of their feature extraction or feature selection methods are supervised and depend on domain-specific expertise. To solve these problems, we proposed a novel framework for the automatic detection of epileptic EEG by using stacked sparse autoencoder (SSAE) with a softmax classifier. The proposed framework firstly learns the sparse and high level representations from the preprocessed data via SSAE, and then send these representations into softmax classifier for training and classification. To verify the performance of this framework, we adopted the epileptic EEG datasets to conduct experiments. The simulation results with an average accuracy of 96 % illustrated the effectiveness of the proposed framework.