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

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2 Author(s)
Jansen, B.H. ; Department of Electrical Engineering, University of Houston-University Park, Houston, TX 77004. ; Cheng, Wei-Kang

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.

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Pattern Analysis and Machine Intelligence, IEEE Transactions on  (Volume:PAMI-9 ,  Issue: 5 )