Automatic Classification of Sleep Stages from EEG Signals Using Riemannian Metrics and Transformer Networks
Résumé
Purpose: In sleep medicine, assessing the evolution of a subject's sleep often involves the costly manual scoring of electroencephalographic (EEG) signals. In recent years, a number of Deep Learning approaches have been proposed to automate this process, mainly by extracting features from said signals. However, despite some promising developments in related problems, such as Brain-Computer Interfaces, analyses of the covariances between brain regions remain underutilized in sleep stage scoring.
Methods: Expanding upon our previous work, we investigate the capabilities of SPDTransNet, a Transformer-derived network designed to classify sleep stages from EEG data through timeseries of covariance matrices. Furthermore, we present a novel way of integrating learned signal-wise features into said matrices without sacrificing their Symmetric Definite Positive (SPD) nature.
Results: Through comparison with other State-of-the-Art models within a methodology optimized for class-wise performance, we achieve a level of performance at or beyond various State-of-the-Art models, both in single-dataset and - particularly - multi-dataset experiments.
Conclusion: In this article, we prove the capabilities of our SPDTransNet model, particularly its adaptability to multi-dataset tasks, within the context of EEG sleep stage scoring - though it could easily be adapted to any classification task involving timeseries of covariance matrices.
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Est une version de hal-04638612 Article Mathieu Seraphim, Alexis Lechervy, Florian Yger, Luc Brun, Olivier Etard. Automatic Classification of Sleep Stages from EEG Signals Using Riemannian Metrics and Transformer Networks. SN Computer Science, 2024, 5 (7), pp.953. ⟨10.1007/s42979-024-03310-5⟩. ⟨hal-04638612⟩