Skip to main content
SpringerLink
Log in

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

  • Conference paper
  • First Online:
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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Berry, R.B., et al.: The AASM manual for the scoring of sleep and associated events. Rules,Terminology Tech. Specifications Darien Illinois Am. Acad. Sleep Med. 176, 2012 (2012)

    Google Scholar 

  2. Björn, R., Jan, B.: About sleep’s role in memory. Physiol. Rev. 93, 681–766 (2013)

    Article  Google Scholar 

  3. Buettner, R., Grimmeisen, A., Gotschlich, A.: High-performance diagnosis of sleep disorders: a novel, accurate and fast machine learning approach using electroencephalographic data. In: Proceedings of the 53rd Hawaii International Conference on System Sciences (2020)

    Google Scholar 

  4. Cooray, N., Andreotti, F., Lo, C., Symmonds, M., Hu, M.T., De Vos, M.: Detection of REM sleep behaviour disorder by automated polysomnography analysis. Clin. Neurophysiol. 130(4), 505–514 (2019)

    Article  PubMed  Google Scholar 

  5. Cooray, N., Andreotti, F., Lo, C., Symmonds, M., Hu, M.T., De Vos, M.: Proof of concept: screening for REM sleep behaviour disorder with a minimal set of sensors. Clin. Neurophysiol. 132(4), 904–913 (2021)

    Article  PubMed  PubMed Central  Google Scholar 

  6. Fayyaz, M., Jaffery, S.S., Anwer, F., Zil-E-Ali, A., Anjum, I.: The effect of physical activity in Parkinson’s disease: a mini-review. Cureus 10(7), e2995 (2018)

    PubMed  PubMed Central  Google Scholar 

  7. Galbiati, A., Verga, L., Giora, E., Zucconi, M., Ferini-Strambi, L.: The risk of neurodegeneration in REM sleep behavior disorder: a systematic review and meta-analysis of longitudinal studies. Sleep Med. Rev. 43, 37–46 (2019)

    Article  PubMed  Google Scholar 

  8. Goldberger, A.L., et al.: Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals. Circulation 101(23), e215–e220 (2000)

    Article  CAS  PubMed  Google Scholar 

  9. Haba-Rubio, J., Frauscher, B., Marques-Vidal, P., et al.: Prevalence and determinants of rapid eye movement sleep behavior disorder in the general population. Sleep 41(2), zsx197 (2018)

    Article  PubMed  Google Scholar 

  10. Hu, M.T.: Rem sleep behavior disorder (RBD). Neurobiol. Dis. 143, 104996 (2020)

    Article  CAS  PubMed  Google Scholar 

  11. Lajnef, T., et al.: Learning machines and sleeping brains: automatic sleep stage classification using decision-tree multi-class support vector machines. J. Neurosci. Methods 250, 94–105 (2015)

    Google Scholar 

  12. López-Garcıa, D., Ruz, M., Ramırez, J., Górriz, J.: Automatic detection of sleep disorders: multi-class automatic classification algorithms based on support vector machines. In: International Conference on Time Series and Forecasting, ITISE 2018, vol. 3, pp. 1270–1280 (2018)

    Google Scholar 

  13. Motamedi-Fakhr, S., Moshrefi-Torbati, M., Hill, M., Hill, C.M., White, P.R.: Signal processing techniques applied to human sleep EEG signals-a review. Biomed. Signal Process. Control 10, 21–33 (2014)

    Article  Google Scholar 

  14. Ngo, H.V.V., Claassen, J., Dresler, M.: Sleep: slow wave activity predicts amyloid-\(\beta \) accumulation. Curr. Biol. 30(22), R1371–R1373 (2020)

    Article  CAS  Google Scholar 

  15. Pavlova, M.K., Latreille, V.: Sleep disorders. Am. J. Med. 132(3), 292–299 (2019)

    Article  Google Scholar 

  16. Peng, H., Long, F., Ding, C.: Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans. Pattern Anal. Mach. Intell. 27(8), 1226–1238 (2005)

    Article  PubMed  Google Scholar 

  17. Rechichi, I., Zibetti, M., Borzì, L., Olmo, G., Lopiano, L.: Single-channel EEG classification of sleep stages based on rem microstructure. Healthc. Technol. Lett. 8(3), 58 (2021)

    Article  PubMed  PubMed Central  Google Scholar 

  18. Schenck, C.H., Bundlie, S.R., Ettinger, M.G., Mahowald, M.W.: Chronic behavioral disorders of human rem sleep: a new category of parasomnia. Sleep 9(2), 293–308 (1986)

    Article  CAS  PubMed  Google Scholar 

  19. Schreiner, S.J., et al.: Reduced regional NREM sleep slow-wave activity is associated with cognitive impairment in Parkinson disease. Front. Neurol. 12, 156 (2021)

    Article  Google Scholar 

  20. Scullin, M.K., Gao, C.: Dynamic contributions of slow wave sleep and REM sleep to cognitive longevity. Curr Sleep Med. Rep. 4(4), 284–293 (2018)

    Article  PubMed  PubMed Central  Google Scholar 

  21. Siegel, J.M.: Rem sleep: a biological and psychological paradox. Sleep Med. Rev. 15(3), 139 (2011)

    Article  PubMed  PubMed Central  Google Scholar 

  22. Simor, P., van der Wijk, G., Nobili, L., Peigneux, P.: The microstructure of rem sleep: why phasic and tonic? Sleep Med. Rev. 52, 101305 (2020)

    Article  PubMed  Google Scholar 

  23. Stefani, A., Högl, B.: Sleep in Parkinson’s disease. Neuropsychopharmacology 45(1), 121–128 (2020)

    Article  PubMed  Google Scholar 

  24. Šušmáková, K., Krakovská, A.: Discrimination ability of individual measures used in sleep stages classification. Artif. Intell. Med. 44(3), 261–277 (2008)

    Article  PubMed  Google Scholar 

  25. Terzano, M.G., et al.: Atlas, rules, and recording techniques for the scoring of cyclic alternating pattern (cap) in human sleep. Sleep Med. 2(6), 537–553 (2001)

    Article  CAS  PubMed  Google Scholar 

  26. Urtnasan, E., Joo, E.Y., Lee, K.H.: Ai-enabled algorithm for automatic classification of sleep disorders based on single-lead electrocardiogram. Diagnostics 11(11), 2054 (2021)

    Article  PubMed  PubMed Central  Google Scholar 

  27. Widasari, E.R., Tanno, K., Tamura, H.: Automatic sleep disorders classification using ensemble of bagged tree based on sleep quality features. Electronics 9(3), 512 (2020)

    Article  Google Scholar 

  28. Xie, L., et al.: Sleep drives metabolite clearance from the adult brain. Science 342(6156), 373–377 (2013)

    Article  CAS  PubMed  Google Scholar 

  29. Yetton, B.D., Niknazar, M., Duggan, K.A., McDevitt, E.A., Whitehurst, L.N., Sattari, N., Mednick, S.C.: Automatic detection of rapid eye movements (REMS): a machine learning approach. J. Neurosci. Methods 259, 72–82 (2016)

    Article  PubMed  Google Scholar 

Download references

Author information

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
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Federica Amato
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Alessandro Cicolin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Gabriella Olmo
    View author publications

    You can also search for this author in PubMed Google Scholar

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

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

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

Download citation

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

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-07703-6

  • Online ISBN: 978-3-031-07704-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation