Sleep Stage Classification Based on Heart Rate Variability Analysis and Model Performance Validation
Zheng Jiewen1,2, Zhang Yuezhou1, Lan Ke1, Liu Xiaoli3, Zhang Zhengbo4#*, Yu Mengsun2#*
1(Bejing SensEcho Science & Technology Co.,Ltd,Beijing 100041,China) 2(Air Force Medical Center,PLA,Beijing 100037,China) 3(School of Biological Science and Medical Engineering,Beihang University,Beijing 100119,China) 4(Department of Biomedical Engineering,Chinese PLA General Hospital,Beijing 100853,China)
Abstract：To conduct sleep stage classification and provide technical support for the application of wearable physiological monitoring technology in the field of chronic disease monitoring and management,a sleep stage classification algorithm based on heart rate variability (HRV) analysis and support vector machine (SVM) was developed. In order to ensure the quality of the training set,67 polysomnography (PSG) records were extracted by experts from the SHHS (Sleep Heart Health Study) PSG database for model training and internal validation. The sleep stages (wake,rapid eye movement and non-rapid eye movement) classified by EEG signals were used as labels to train the SVM model. Totally 86 features were derived from HRV analysis,including time domain,frequency domain and nonlinear domain. To test the generalization of the model,another 939 PSG records were further randomly extracted from the SHHS PSG database for model external validation. The accuracy of the 5-fold cross-validation on the training dataset of the 67 PSG records was 84.00%±1.33%,with a Kappa coefficient:0.70±0.03,and the accuracy of the algorithm on the 939 PSG records was 76.10%±10.8%,with a Kappa coefficient:0.57±0.15. The accuracy and the Kappa coefficient increased to 82.00%±5.6% and 0.67±0.14 when some records were excluded from the test dataset,including 110 records with abnormal RR intervals and 29 records with apparent abnormal sleep structures. These results showed that the model of heart rate variability analysis based sleep stage classification proposed in this paper exhibited a good performance,and the external validation by a dataset with large sample size demonstrated the generalization of the model.
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