Automatic Sleep Stage Classification on EEG Signals Using Time-Frequency Representation

Paul Dequidt, Mathieu Seraphim, Alexis Lechervy, Ivan Igor Gaez, Luc Brun, Olivier Etard

Abstract: Sleep stage scoring based on electroencephalogram (EEG) signals is a repetitive task required for basic and clinical sleep studies. Sleep stages are defined on 30 s EEG-epochs from brainwave patterns present in specific frequency bands. Time-frequency representations such as spectrograms can be used as input for deep learning methods. In this paper we compare different spectrograms, encoding multiple EEG channels, as input for a deep network devoted to the recognition of image’s visual patterns. We further investigate how contextual input enhance the classification by using EEG-epoch sequences of increasing lengths. We also propose a common evaluation framework to allow a fair comparison between state-of-art methods. Evaluations performed on a standard dataset using this unified protocol show that our method outperforms four state-of-art methods.