Automatic Classification of Sleep Stages from EEG Signals Using Riemannian Metrics and Transformer Networks - Archive ouverte HAL
Journal Articles SN Computer Science Year : 2024

Automatic Classification of Sleep Stages from EEG Signals Using Riemannian Metrics and Transformer Networks

Abstract

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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Dates and versions

hal-04638612 , version 1 (08-07-2024)

Identifiers

  • HAL Id : hal-04638612 , version 1

Cite

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, In press. ⟨hal-04638612⟩
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