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.
This study is co-funded by the Normandy County Council and the European Union (PredicAlert European Project - FEDER fund). Part of this work was performed using computing resources of CRIANN (Normandy, France). This work was performed using HPC resources from GENCI-IDRIS (Grant 2022-102446).
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Dequidt, P., Seraphim, M., Lechervy, A., Gaez, I.I., Brun, L., Etard, O. (2023). Automatic Sleep Stage Classification on EEG Signals Using Time-Frequency Representation. In: Juarez, J.M., Marcos, M., Stiglic, G., Tucker, A. (eds) Artificial Intelligence in Medicine. AIME 2023. Lecture Notes in Computer Science(), vol 13897. Springer, Cham. https://doi.org/10.1007/978-3-031-34344-5_30
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