摘要
睡眠障碍会严重影响人们的日常生活,因此睡眠的早期监测对睡眠疾病的预防和诊断有重要意义.采用自行研制的便携式多导睡眠监护仪,开展了 103人次的居家夜间睡眠数据收集(含脑电、眼电、肌电和心电信号).然后,从同步采集的心电数据RR间期中提取时域、频率和非线性特征,组合出最高达426个心率变异性(HRV)特征,基于Xgboost算法构建模型对睡眠中的清醒期(wake)、非快速眼动Ⅰ期(N1)、非快速眼动Ⅱ期(N2)、非快速眼动Ⅲ期(N3)和快速眼动期(REM)进行五分类(wake、N1、N2、N3、REM)、三分类(wake+N1、REM、N2+N3)和二分类(wake、N1+N2+N3+REM)预测,并与脑电图睡眠分期标签进行验证.最后,五分类、三分类和二分类测试结果准确率分别达到84.0％、89.1％和95.2％,F1-score达到83.2％、88.9％和94.9％,为同类模型研究中表现最佳.说明HRV与睡眠阶段具有良好的相关性,基于便携式设备收集数据构建的算法模型可以较好地识别睡眠状态.
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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