基于心率变异性分析的睡眠分期方法研究  

Study on Sleep Staging Methods Based on Heart Rate Variability Analysis

作　　者：王金海[1,2] 孙微[1] 韦然[1,2] 赵晓赟[3] 国海丁 王慧泉[1,2] 

机构地区：[1]天津工业大学电子与信息工程学院,天津300387 [2]天津市医学电子诊疗技术工程中心,天津300387 [3]天津市胸科医院,天津300350

出　　处：《生物医学工程学杂志》2016年第3期420-425,共6页Journal of Biomedical Engineering

基　　金：天津市应用基础与前沿技术研究计划资助项目(13JCYBJC37800);教育部留学归国科研启动基金(第49批)

摘　　要：为实现对不同阶段睡眠的快速、便捷分期,本文实验分析了心率变异性(HRV)与睡眠分期的相关性,利用支持向量机(SVM)实现了基于HRV信息的睡眠自动分期的算法。对天津市胸科医院的33例临床心电数据进行了R-R提取和主成分分析(PCA),并利用SVM对睡眠中的不同阶段进行建模和预测,将分期的预测结果与基于脑电金标准的睡眠分期标注结果比对,对于三期睡眠的预测准确度超过80%,说明HRV与睡眠各期具有良好的相关性。该方法是对传统睡眠分期方法的一种补充,具有实际使用价值。In order to realize sleep staging automatically and conveniently, we used support vector machine （SVM） to analyze the correlation between heart rate variability and sleep stage experimentally. R-R intervals （RRIs） from 33 cases of sleep clinical data of Tianjin Thoracic Hospital were extracted and analyzed by principal component analysis （PCA）. The SVM method was used to establish the model and predict the five sleep stages. The prediction accuracy of three-sleep-stage was higher than 80%, in contrast to sleep scoring annotations marked by physiological experts based on electroencephalogram （EEG） golden standard. The result showed that there was a good correlation between heart rate variability and sleep staging. This method is an important supplement to the traditional sleep staging method and has a great value for clinical application.

关 键 词：心率变异性 主成分分析 支持向量机 睡眠分期 

分 类 号：R740[医药卫生—神经病学与精神病学]