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CIMCA
2005
IEEE

Automatic Sleep Staging using Support Vector Machines with Posterior Probability Estimates

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Automatic Sleep Staging using Support Vector Machines with Posterior Probability Estimates
This paper describes attempts at constructing an automatic sleep stage classifier using EEG recordings. Three different feature extraction schemes were compared together with two different pattern classifiers, the recently introduced support vector machine and the well known knearest neighbor classifier. Using estimates of posterior probabilities for each of the sleep stages it was possible to devise a simple post-processing rule which leads to improved accuracy. Compared to a human expert the accuracy of the best classifier is 81%.
Steinn Gudmundsson, Thomas Philip Runarsson, Sven
Added 24 Jun 2010
Updated 24 Jun 2010
Type Conference
Year 2005
Where CIMCA
Authors Steinn Gudmundsson, Thomas Philip Runarsson, Sven Sigurdsson
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