MACHINE LEARNING OF HUMAN SLEEP PATTERNS BASED ON STAGE BOUT DURATIONS

Francis W. Usher, Chiying Wang, Sergio A. Alvarez, Carolina Ruiz, Majaz Moonis

Abstract

This paper explores the discovery of patterns in human sleep data based on the duration statistics of continuous bouts in individual sleep stages during a full night of sleep. Hypnograms from 244 patients are examined. Stage bout durations are described in terms of the quartiles of their stage bout duration distributions, yielding 15 descriptive variables corresponding to wake after sleep onset, NREM stage 1, NREM stage 2, slow wave sleep, and REM sleep. Unsupervised Expectation-Maximization clustering is employed 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, the values of which differ significantly from those of other groups (p<0.05). Among other sleep-related and health-related variables, several are shown to be significantly different among the bout duration groups found through clustering, while multivariate linear regression fails to yield good predictive models based on the same bout duration variables used in the clustering analysis. This provides an example of the successful use of machine learning to uncover naturally occurring dynamical patterns in sleep data that can also provide sleep-based indicators of health.

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Paper Citation


in Harvard Style

W. Usher F., Wang C., A. Alvarez S., Ruiz C. and Moonis M. (2012). MACHINE LEARNING OF HUMAN SLEEP PATTERNS BASED ON STAGE BOUT DURATIONS . In Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2012) ISBN 978-989-8425-88-1, pages 71-80. DOI: 10.5220/0003782500710080


in Bibtex Style

@conference{healthinf12,
author={Francis W. Usher and Chiying Wang and Sergio A. Alvarez and Carolina Ruiz and Majaz Moonis},
title={MACHINE LEARNING OF HUMAN SLEEP PATTERNS BASED ON STAGE BOUT DURATIONS},
booktitle={Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2012)},
year={2012},
pages={71-80},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003782500710080},
isbn={978-989-8425-88-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2012)
TI - MACHINE LEARNING OF HUMAN SLEEP PATTERNS BASED ON STAGE BOUT DURATIONS
SN - 978-989-8425-88-1
AU - W. Usher F.
AU - Wang C.
AU - A. Alvarez S.
AU - Ruiz C.
AU - Moonis M.
PY - 2012
SP - 71
EP - 80
DO - 10.5220/0003782500710080