Sleep staging is the basis of scientific evaluation of sleep. It plays vital role in the early diagnosis and intervention of sleep disorders.The automatic sleep staging system was constructed by firstly using ensemble empirical mode decomposition (EEMD) to the electroencephalography (EEG) signals and then extracting both linear and nonlinear features from the decomposed intrinsic mode functions (IMFs).The features were fed into machine learning models based on five different algorithms. After applying the proposed method on sleep EEG data of 111 healthy subjects.It can be found that the proposed method can yield to automatic sleep staging systems with substantial performance on several machine learning models. Total accuracies of 81.0% and 79.7% were achieved by the model based on extreme gradient boosting algorithm, respectively in 4-label and 5-label staging.

automatic sleep staging; ensemble empirical mode decomposition; machine learning