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A study of sleep stage classification based on permutation entropy for electroencephalogram

Li, G.; Fan, Y.; Pang, Q.

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi 26(4): 869-872

2009

ISSN/ISBN: 1001-5515
PMID: 19813629
Document Number: 632159
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%.

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