title: Classification of sleep patterns by means of markov modeling and correspondence analysis.
abstract: 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.
authors.affiliation: Department of Electrical Engineering, University of Houston-University Park, Houston, TX 77004.
authors.forename: B H
authors.initials: BH
authors.lastname: Jansen
authors.forename: W K
authors.initials: WK
authors.lastname: Cheng