基于心率变异性信号的睡眠呼吸暂停检测方法  

Sleep apnea detection method based on heart rate variability signal

作　　者：牛艳玲 刘健 朱一荻 李翔 李锦[1] 凤飞龙[1] NIU Yanling;LIU Jian;ZHU Yidi;LI Xiang;LI Jin;FENG Feilong(School of Physics and Information Technology,Shaanxi Normal University,Xi′an 710119,Shaanxi,China)

机构地区：[1]陕西师范大学物理学与信息技术学院,陕西西安710119

出　　处：《陕西师范大学学报（自然科学版）》2020年第6期26-32,共7页Journal of Shaanxi Normal University：Natural Science Edition

基　　金：国家自然科学基金(11974231,11774212);陕西省自然科学基金(2020SF-134)。

摘　　要：提出了一种仅使用心率变异性信号来自动检测睡眠呼吸暂停的方法。首先,从心电信号中提取心率变异性信号,并进行异常值处理和分段;其次,利用基于方差分析(analysis of variance,ANOVA)和最大相关-最小冗余(max-relevance and min-redundancy,mRMR)的两阶段特征选择策略获得特征向量;最后,采用五折交叉验证训练随机森林分类器。结果表明:本算法使用9个特征将每分钟“epoch”信号分类为呼吸暂停或正常,在测试集中获得的睡眠呼吸暂停识别的平均准确率为80%,Kappa系数0.61;该方法具有无创且低成本的特性,有利于便携式睡眠检测设备的硬件实现。The method that heart rate variability signals automatic detecting sleep apnea is proposed.The method includes three stages.First,extract heart rate variability signals from ECG signals,and perform outlier processing and segmentation.Secondly,the two-stage feature selection strategy based on analysis of variance(ANOVA)and max-relevance and min-redundancy(mRMR)proposed in this thesis is used to obtain feature vectors.Finally,a random forest classifier is trained using 5-fold cross-validation.The algorithm uses 9 features to classify each 1-minute“epoch”as apnea or normal.The average accuracy of sleep apnea recognition obtained in the test set is 80%,and the Kappa coefficient is 0.61.This method has the characteristics of non-invasive and low cost,which is beneficial to the hardware implementation of portable sleep detection devices.

关 键 词：睡眠呼吸暂停综合症 心率变异性 特征选择 随机森林 

分 类 号：R318.6[医药卫生—生物医学工程]