title: [A study of sleep stage classification based on permutation entropy for electroencephalogram].
abstract: This paper presents a new method for automatic sleep stage classification which is based on the EEG permutation entropy. The EEG permutation entropy has notable distinction in each stage of sleep and manifests the trend of regular transforming. So it can be used as features of sleep EEG in each stage. Nearest neighbor is employed as the pattern recognition method to classify the stages of sleep. Experiments are conducted on 750 sleep EEG samples and the mean identification rate can be up to 79.6%.
authors.affiliation: Wenthou Medical College, Wenzhou 325000, China. hatewww518@sina.com
authors.forename: Gu
authors.initials: G
authors.lastname: Li
authors.forename: Yingle
authors.initials: Y
authors.lastname: Fan
authors.forename: Quan
authors.initials: Q
authors.lastname: Pang
mesh_terms: D002965:Classification / Q000379:methods; D004569:Electroencephalography / Q000379:methods*; D019277:Entropy; D006801:Humans; D010363:Pattern Recognition, Automated; D012815:Signal Processing, Computer-Assisted; D012894:Sleep Stages / Q000502:physiology*