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Comparison between five classifiers for automatic scoring of human sleep recordings

Abstract : The aim of this work is to compare the performances of 5 classifiers (linear and quadratic classifiers, k nearest neighbors, Parzen kernels and neural network) to score a set of 8 biological parameters extracted from EEG and EMG, in six classes corresponding to different sleep stages. The data base is composed of 17265 epochs of 20s recorded from 4 patients. Each epoch has been classified by an expert. In order to evaluate the classifiers, learning and testing sets of fixed size are randomly drawn and are used to train and test the classifiers. After several trials, an estimation of the misclassification percentage and its variability is obtained (optimistically and pessimistically). Data transformations toward normal distribution are explored as an approach to deal with extreme values. It is shown that these transformations improve significantly the results of the classifiers based on data proximity.
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Contributor : Guillaume Becq Connect in order to contact the contributor
Submitted on : Thursday, January 11, 2007 - 5:44:10 PM
Last modification on : Wednesday, October 20, 2021 - 12:47:23 AM


  • HAL Id : hal-00123995, version 1


Guillaume Becq, Sylvie Charbonnier, Florian Chapotot, Alain Buguet, Lionel Bourdon, et al.. Comparison between five classifiers for automatic scoring of human sleep recordings. Saman Halgamuge and Lipo Wang. Classification and Clustering for Knowledge Discovery, Springer Verlag, pp.113, 2005. ⟨hal-00123995⟩



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