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

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Abstract

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. 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. 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. 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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Data Availibility Statement

Our code is publicly available in the following repository: github.com/MathieuSeraphim/SPDTransNet_plus. The Montreal Archive of Sleep Studies (MASS) dataset is available upon request, pending approval from the Ethics Review Board (ERB) of the MASS host [66]. The Dreem DOD-H dataset is freely available online, and downloading instructions can be found in the authors’ GitHub repository [27].

Notes

  1. Electrical signals acquired from electrodes located around the brain, often non-invasively.

  2. As implemented in [61].

  3. As can be seen in the equivalent matrices computed for each recording and channel, available in our GitHub repository.

  4. Following the common convention of referring to the vector inputs of Transformer-based architectures as “tokens".

  5. Given a token size d and a number of attention heads h for a Transformer encoder, we found that imposing \(\frac{d}{h} \ge 32\) yielded better results.

  6. Here, the logarithm is defined from SPD(m) to Sym(m), and the exponential from Sym(p) to SPD(p).

  7. The largest possible number of epochs ignored by DeepSleepNet [14] at the end of a test set recording.

  8. All MASS-SS3 subjects use a linked-ear reference (LER), as do some MASS-SS1 subjects, the others using a computed linked-ear (CLE) reference.

  9. Located behind the ear in the opposite hemisphere.

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Acknowledgements

This work was granted access to the HPC resources of IDRIS (Institut du Développement et des Ressources en Informatique Scientifique) under the allocation 2022-AD010613618 made by GENCI (Grand Ëquipement National de Calcul Intensif), and to the computing resources of CRIANN (Centre Régional Informatique et d’Applications Numériques de Normandie, Normandy, France).

Funding

This work was granted access to computing resources of IDRIS and CRIANN (see Acknoledgements). Mathieu Seraphim is supported by the French National Research Agency (ANR) and Region Normandie under grant HAISCoDe. This work was partially carried out during a CNRS leave at GREYC of Florian Yger.

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Authors and Affiliations

  1. Normandie Univ UNICAEN, ENSICAEN, CNRS, GREYC, 14000, Caen, France

    Mathieu Seraphim, Alexis Lechervy & Luc Brun

  2. Normandie Université, UNICAEN, INSERM, COMETE, CYCERON, CHU Caen, 14000, Caen, France

    Olivier Etard

  3. LAMSADE, CNRS, PSL University Paris-Dauphine, 75016, Paris, France

    Florian Yger

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Contributions

All authors contributed to the study conception and design. Experiments were performed by Mathieu Seraphim, under the supervision of Alexis Lechervy, Florian Yger, Luc Brun and Olivier Etard. The first draft of the manuscript was written by Mathieu Seraphim and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Research Involving Human

Both utilized datasets are composed of anonymized recordings of human biological signals. The MASS recordings were anonymized and distributed in accordance with guidelines from the MASS research team’s ethics board [66]. The Dreem DOD-H dataset was compiled and made publicly available with approval from the Committees of Protection of Persons (CPP), and declared and carried out in accordance with French law [27]. No further biological data was utilized in the production of this paper.

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Seraphim, M., Lechervy, A., Yger, F. et al. Automatic Classification of Sleep Stages from EEG Signals Using Riemannian Metrics and Transformer Networks. SN COMPUT. SCI. 5, 953 (2024). https://doi.org/10.1007/s42979-024-03310-5

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