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International Joint Conference on Biomedical Engineering Systems and Technologies

BIOSTEC 2011: Biomedical Engineering Systems and Technologies pp 380–394Cite as

Similarity Grouping of Human Sleep Recordings Using EEG and ECG

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 273))

Abstract

Characterizing variations in sleep stage composition is important in the scientific study of sleep. We use clustering, a form of unsupervised machine learning, to seek naturally occurring types within a collection of records that describe the sleep stage composition of 244 all-night human sleep studies. The results uncover a hierarchy of sleep composition types differentiated primarily by sleep efficiency or total sleep time and by the relative proportion of slow-wave sleep. The potential significance of these sleep type clusters for sleep medicine is suggested by associations between sleep type and health-related variables such as body-mass index, smoking frequency, and heart disease. EEG and ECG features, including spectral power distribution and measures of heart-rate variability, differ significantly among sleep types. The EEG signal provides sufficient information for an approximate reconstruction of the sleep type clusters, while ECG alone is found to be insufficient.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Worcester Polytechnic Institute, 100 Institute Road, Worcester, MA, 01609, U.S.A.

    Amro Khasawneh, Carolina Ruiz & Shivin Misra

  2. Department of Computer Science, Boston College, 140 Commonwealth Avenue, Chestnut Hill, MA, 02467, U.S.A.

    Sergio A. Alvarez

  3. Department of Neurology, University of Massachusetts Medical School, 55 Lake Avenue North, Worcester, MA, 01655, U.S.A.

    Majaz Moonis

Authors
  1. Amro Khasawneh
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  2. Sergio A. Alvarez
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  3. Carolina Ruiz
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  4. Shivin Misra
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  5. Majaz Moonis
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Editor information

Editors and Affiliations

  1. IST - Technical University of Lisbon, Av.Rovisco Pais, 1, 1049-001, Lisbon, Portugal

    Ana Fred

  2. Departament of Systems and Informatics, Polytechnic Institute of Setúbal – INSTICC, Rua do Vale de Chaves - Estefanilha, 2910-761, Setúbal, Portugal

    Joaquim Filipe

  3. Institute of Telecommunications, Av. Rovisco Pais, 1, 1049-001, Lisboa, Portugal

    Hugo Gamboa

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Khasawneh, A., Alvarez, S.A., Ruiz, C., Misra, S., Moonis, M. (2013). Similarity Grouping of Human Sleep Recordings Using EEG and ECG. In: Fred, A., Filipe, J., Gamboa, H. (eds) Biomedical Engineering Systems and Technologies. BIOSTEC 2011. Communications in Computer and Information Science, vol 273. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29752-6_28

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  • DOI: https://doi.org/10.1007/978-3-642-29752-6_28

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29751-9

  • Online ISBN: 978-3-642-29752-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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