Towards automated sleep classification in infants using symbolic and subsymbolic approaches.
The paper addresses the problem of automatic sleep classification. A special effort is made to find a method of extracting reasonable descriptions of the individual sleep stages from sample measurements of EGG, EMG, EOG, etc., and from a classification of these measurements provided by an expert. The method should satisfy three requirements: classification accuracy, interpretability of the results, and the ability to select the relevant and discard the irrelevant variables. The solution suggested in this paper consists of a combination of the subsymbolic algorithm LVQ with the symbolic decision tree generator ID3. Results demonstrating the feasibility and utility of our approach are also presented.
Ludwig-Boltzmann Institute of Medical Informatics and Neuroinformatics, Graz University of Technology.
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M
Kubat
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D
Flotzinger
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G
Pfurtscheller
D000465:Algorithms; D005103:Expert Systems; D005260:Female; D006801:Humans; D007223:Infant; D008297:Male; D017286:Polysomnography / Q000145:classification* / Q000295:instrumentation; D012016:Reference Values; D012815:Signal Processing, Computer-Assisted / Q000295:instrumentation*; D012894:Sleep Stages / Q000502:physiology*; D012895:Sleep, REM / Q000502:physiology