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Authors: Francis W. Usher 1 ; Chiying Wang 1 ; Sergio A. Alvarez 2 ; Carolina Ruiz 1 and Majaz Moonis 3

Affiliations: 1 Worcester Polytechnic Institute, United States ; 2 Boston College, United States ; 3 U. of Massachusetts Medical School, United States

Keyword(s): Sleep, Bout duration, Sleep dynamics, Data mining, Clustering, Machine learning.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Business Analytics ; Cardiovascular Technologies ; Clinical Problems and Applications ; Computing and Telecommunications in Cardiology ; Data Engineering ; Data Mining ; Databases and Information Systems Integration ; Datamining ; Enterprise Information Systems ; Health Engineering and Technology Applications ; Health Information Systems ; Medical and Nursing Informatics ; Pattern Recognition and Machine Learning ; Sensor Networks ; Signal Processing ; Soft Computing ; Software Systems in Medicine

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 model s 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. (More)

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Paper citation in several formats:
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 (BIOSTEC 2012) - HEALTHINF; ISBN 978-989-8425-88-1; ISSN 2184-4305, SciTePress, pages 71-80. DOI: 10.5220/0003782500710080

@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 (BIOSTEC 2012) - HEALTHINF},
year={2012},
pages={71-80},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003782500710080},
isbn={978-989-8425-88-1},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the International Conference on Health Informatics (BIOSTEC 2012) - HEALTHINF
TI - MACHINE LEARNING OF HUMAN SLEEP PATTERNS BASED ON STAGE BOUT DURATIONS
SN - 978-989-8425-88-1
IS - 2184-4305
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
PB - SciTePress