关键词: 睡眠分期, 模糊特征, 复杂度, 多尺度熵, 模糊熵

Abstract: The method of EEG fuzzy features classification is used to carry out sleep staging. Firstly, EEG preprocessing filters out interference and noise. Secondly, fuzzy entropy algorithm, multiscale entropy algorithm and complexity algorithm are used to extract EEG characteristic parameters. Then the Least Squares Support Vector Machine（LS-SVM） is taken to classify the characteristic parameters, and the sleep process is divided into wakefulness, light sleep, deep sleep, Rapid Eye Movement（REM）, after that sleep staging accuracy is obtained. Finally, 2,000 group sleep EEG samples are tested by the above method, and then staging results of experts are compared with the automatic stage. Taking the complexity as the input of LS-SVM, the average accuracy rate of sleep stage classification is 92.65%, which is higher than the rate of fuzzy entropy or multiscale entropy as the input. The results show that the complexity of the fuzzy feature extraction based on the characteristic parameters can be used as a valid basis for sleep staging. Labor costs is reduced under the premise of ensuring the accuracy.

Key words: sleep staging, fuzzy feature, complexity, multiscale entropy, fuzzy entropy