1(School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610000,China); 2(School of Computer Science, Chengdu University of Information Technology, Chengdu 610000, China)
Abstract：Sleep disorders seriously affect life quality, therefore, early sleep monitoring is important for the prevention and diagnosis of sleep diseases. In this paper, we proposed a portable polysomnography and completed in-home sleep data collection for 103 nights by this system, including EEG, EOG, EMG and ECG signals. Time-domain, frequency, and nonlinear features were extracted from the RR intervals of the synchronously acquired ECG data, and up to 426 heart rate variability (HRV) features were combined to construct models based on the Xgboost algorithm to predict wake, non-rapid eye movement I(N1), non-rapid eye movement II (N2), non-rapid eye movement III(N3), and rapid eye movement (REM) stages of sleep with five-classification (wake, N1, N2, N3, and REM), three-classification (wake+N1, REM, N2+N3), and two-classification (wake, N1+N2+N3+REM), and to validate them with the EEG sleep staging labels. Among these, the accuracy of the five-classification, three-classification and two-classification test results reached 84.0%, 89.1% and 95.2%, respectively, and the F1-score reached 83.2%, 88.9% and 94.9%, which was the best performance among other model studies of this kind. It indicated that HRV had good correlation with sleep stages, and the HRV-based algorithmic models constructed based on the data collected from portable devices identified the sleep states well.
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