Classification of Sleep Patterns by Means of Markov Modeling and Correspondence Analysis

Shown is how correspondence analysis can be used to track changes in an individuals' sleep pattern. Correspondence analysis was applied to sleep stage transition matrices computed from all-night sleep of normal, obese, and apnoetic subjects. Differences between the groups, and intraindividual c...

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Bibliographic Details
Published in:IEEE transactions on pattern analysis and machine intelligence 1987-09, Vol.PAMI-9 (5), p.707-710
Main Authors: Jansen, Ben H., Cheng, Wei-Kang
Format: Article
Language:English
Subjects:
Biological and medical sciences
Clustering
correspondence analysis
Electroencephalography
Fractals
Frequency
Fundamental and applied biological sciences. Psychology
Infrared detectors
Layout
Markov chains
Pattern analysis
Pattern recognition
Satellites
sleep pattern classification
sleep patterns
Sleep. Vigilance
Solid modeling
Stochastic processes
transition probability matrices
Vertebrates: nervous system and sense organs
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Summary:Shown is how correspondence analysis can be used to track changes in an individuals' sleep pattern. Correspondence analysis was applied to sleep stage transition matrices computed from all-night sleep of normal, obese, and apnoetic subjects. Differences between the groups, and intraindividual changes in sleep patterns could be visualized better than with a x2-based clustering approach.
ISSN:0162-8828
1939-3539
DOI:10.1109/TPAMI.1987.4767968