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Single-Channel EEG Detection of REM Sleep Behaviour Disorder: The Influence of REM and Slow Wave Sleep

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Bioinformatics and Biomedical Engineering (IWBBIO 2022)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 13346))

Abstract

Sleep Disorders have received much attention in recent years, as they are related to the risk and pathogenesis of neurodegenerative diseases. Notably, REM Sleep Behaviour Disorder (RBD) is considered an early symptom of \(\alpha \)-synucleinopathies, with a conversion rate to Parkinson’s Disease (PD) up to 90%. Recent studies also highlighted the role of disturbed Non-REM Slow Wave Sleep (SWS) in neurodegenerative diseases pathogenesis and its link to cognitive outcomes in PD and Dementia. However, the diagnosis of sleep disorders is a long and cumbersome process. This study proposes a method for automatically detecting RBD from single-channel EEG data, by analysing segments recorded during both REM sleep and SWS. This paper inspects the underlying microstructure of the two stages and includes a comparison of their performance to discuss their potential as markers for RBD. Machine Learning models were employed in the binary classification between healthy and RBD subjects, with an 86% averaged accuracy on a 5-fold cross-validation when considering both stages. Besides, SWS features alone proved promising in detecting RBD, scoring a 91% sensitivity (RBD class). These findings suggest the applicability of an EEG-based, low-cost, automatic detection of RBD, leading to potential use in the early diagnosis of neurodegeneration, thus allowing for disease-modifying interventions.

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Authors and Affiliations

  1. Department of Control and Computer Engineering, Politecnico di Torino, Turin, Italy

    Irene Rechichi, Federica Amato & Gabriella Olmo

  2. Department of Neuroscience, Sleep Disorders Centre, University of Turin, Turin, Italy

    Alessandro Cicolin

Authors
  1. Irene Rechichi
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  2. Federica Amato
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  3. Alessandro Cicolin
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  4. Gabriella Olmo
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Corresponding author

Correspondence to Irene Rechichi .

Editor information

Editors and Affiliations

  1. Marcelina Siebold Guest Relations Dept., University of Granada, Granada, Spain

    Ignacio Rojas

  2. Faculty of Sciences, University of Granada, Granada, Spain

    Olga Valenzuela

  3. ETSIIT. CITIC-UGR, University of Granada, Granada, Spain

    Fernando Rojas

  4. ETSIIT, University of Granada, Granada, Spain

    Luis Javier Herrera

  5. University of Granada, Granada, Spain

    Francisco Ortuño

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Rechichi, I., Amato, F., Cicolin, A., Olmo, G. (2022). Single-Channel EEG Detection of REM Sleep Behaviour Disorder: The Influence of REM and Slow Wave Sleep. In: Rojas, I., Valenzuela, O., Rojas, F., Herrera, L.J., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2022. Lecture Notes in Computer Science(), vol 13346. Springer, Cham. https://doi.org/10.1007/978-3-031-07704-3_31

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  • DOI: https://doi.org/10.1007/978-3-031-07704-3_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-07703-6

  • Online ISBN: 978-3-031-07704-3

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