author = "Paul Dequidt and Mathieu Seraphim and Alexis Lechervy and Ivan Igor Gaez and Luc Brun and Olivier Etard", editor="Juarez, Jose M. and Marcos, Mar and Stiglic, Gregor and Tucker, Allan", title = "Automatic sleep stage classification on EEG signals using time-frequency representation", booktitle = "Proceedings of {AIME} 2023, Slovenia", month = jun, year = 2023, publisher = "Springer Nature", pages="250-259", theme="pattern", url ={Springer:=https://link.springer.com/chapter/10.1007/978-3-031-34344-5_30, HAL:=https://hal.science/hal-04249277v1/document}, 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 seconds 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."