Objective. The sleep monitoring with Polysomnography (PSG) severely degrades the sleep quality. In order to reduce the load of sleep monitoring, an approach to automatic sleep stage classification without electroencephalogram (EEG) was proposed. Approach. Totally 124 records from the public dataset ISRUC-Sleep with AASM standard were used, in which only 10 records were from the healthy group while the rest ones were from sleep disorder groups. The 124 records were collected from 116 subjects (8 subjects with two records for each subject, others with one record per subject) with their ages range in [20, 85] years. Totally 108 features were extracted from two-channel electrooculogram (EOG), and 6 features were extracted from one-channel electromyogram (EMG). A novel ‘quasi-normalization’ method was proposed and used for feature normalization. Then the random forest (RF) was used to classify five stages, including wakefulness, REM sleep, N1 sleep, N2 sleep and N3 sleep. Main results. 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). As a reference for AASM standard using computer assisted method, 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). Significance. The combination of EOG and EMG can reduce the load of sleep monitoring, and achieves comparable performances with the "gold standard" signals of EEG, EOG and EMG on sleep stage classification.
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.
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