Automatic Sleep Stage Classification Based on Two-channel EOG and One-channel EMG

Abstract The sleep monitoring with PSG severely degrades the sleep quality. In order to simplify the hygienic processing and reduce the load of sleep monitoring, an approach to automatic sleep stage classification without electroencephalogram (EEG) was explored. Totally 108 features from two-channel electrooculogram (EOG) and 6 features from one-channel electromyogram (EMG) were extracted. After feature normalization, the random forest (RF) was used to classify five stages, including wakefulness, REM sleep, N1 sleep, N2 sleep and N3 sleep. Using 114 normalized features from the combination of EOG (108 features) and EMG (6 features), the Cohen’s kappa coefficient was 0.749 and the accuracy was 80.8% by leave-one -out cross-validation (LOOCV) for 124 records from ISRUC-Sleep. As a reference for AASM standard, the Cohen’s kappa coefficient was 0.801 and the accuracy was 84.7% for the same dataset based on 438 normalized features from the combination of EEG (324 features), EOG (108 features) and EMG (6 features). In conclusion, the approach by EOG+EMG with the normalization can reduce the load of sleep monitoring, and achieves comparable performances with the "gold standard" EEG+EOG+EMG on sleep classification.