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Clustering of Human Sleep Recordings Using a Quantile Representation of Stage Bout Durations

  • Conference paper
Biomedical Engineering Systems and Technologies (BIOSTEC 2012)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 357))

Abstract

In this paper, a condensed representation of stage bout durations based on the q-quantiles of the duration distributions is used as a basis for the discovery of duration-related patterns in human sleep data. A collection of 244 all-night hypnograms is studied. Quartiles (q = 4) provide a good tradeoff between representational detail and sample variation. 15 descriptive variables are obtained that correspond to the bout duration quartiles of wake after sleep onset, NREM stage 1, NREM stage 2, slow wave sleep, and REM sleep. EM clustering is used to identify distinct groups of hypnograms based on stage bout durations. Each group is shown to be characterized by bout duration quartiles of specific sleep stages, with statistically significant differences among groups (p < 0.05). Several sleep-related and health-related variables are shown to be significantly different among the bout duration groups found through clustering. In contrast, multivariate linear regression fails to yield good predictive models based on the same bout duration variables used in the clustering analysis. This work demonstrates that machine learning techniques are capable of uncovering naturally occurring dynamical patterns in sleep data that also provide sleep-based indicators of health.

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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.

    Chiying Wang, Francis W. Usher & Carolina Ruiz

  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

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  1. Chiying Wang
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  2. Francis W. Usher
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  3. Sergio A. Alvarez
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  4. Carolina Ruiz
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  5. Majaz Moonis
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Editor information

Editors and Affiliations

  1. University of Porto, Portugal

    Joaquim Gabriel

  2. Institute of Information Theory and Automation of the ASCR, Pod vodárenskou věží 4, CZ-182 08, Prague 8, Czech Republic

    Jan Schier

  3. Dept. of Electrical Engineering, ESAT-SCD(SISTA), Katholieke Universiteit Leuven, Belgium

    Sabine Van Huffel

  4. University of Toulouse, France

    Emmanuel Conchon

  5. University of Coimbra, Portugal

    Carlos Correia

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

    Ana Fred

  7. Institute of Telecommunication, Lisboa, Portugal

    Hugo Gamboa

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Wang, C., Usher, F.W., Alvarez, S.A., Ruiz, C., Moonis, M. (2013). Clustering of Human Sleep Recordings Using a Quantile Representation of Stage Bout Durations. In: Gabriel, J., et al. Biomedical Engineering Systems and Technologies. BIOSTEC 2012. Communications in Computer and Information Science, vol 357. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38256-7_25

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  • DOI: https://doi.org/10.1007/978-3-642-38256-7_25

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-38255-0

  • Online ISBN: 978-3-642-38256-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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