多尺度熵睡眠呼吸暂停程度的分析

摘要

当前就睡眠呼吸暂停症状的研究对心血管疾病的预测有重要的临床意义.选用Apnea-ECG database里的37个研究对象,根据呼吸暂停低通气指数(apnea hypopnea index,AHI)分为四类:正常群体(AHI<5)、轻度睡眠呼吸暂停(5≤AHI<15)、中度睡眠呼吸暂停(15≤AHI<30)、重度睡眠呼吸暂停(AHI≥30).选取样本整个数据中间时刻1 h心电信号(ECG)数据,以保证数据是处于研究对象深度睡眠下测得的.利用Costa等提出的多尺度熵(multiscale entropy,MSE)算法应用于ECG的RR间隔,来分析四类睡眠呼吸暂停的不同程度.根据多尺度熵指数(multiscale entropy index, MEI)定义MEI1~3和MEI13~15.研究结果表明MEI1~3和MEI13~15能够很好地区分正常睡眠群体、轻度睡眠呼吸暂停患者、中度和重度呼吸暂停患者;并且MEI1~3和AHI二者具有良好的负相关性(R2=0.279,P<0.05).%The current study of sleep apnea symptoms has important clinical significance in predicting cardiovascular disease.37 subjects in Apnea-ECG database are selected and divided into four groups on account of apnea hypopnea index (AHI):normal subjects (AHI<5),mild sleep apnea (5≤AHI<15), moderate sleep apnea (15≤AHI<30),severe sleep apnea (AHI≥30).Select the 1 hour ECG data which is in the middle time of entire sample data to ensure that it is measured in the study subjects under deep sleep.The multiscale entropy algorithm proposed by Costa et al.is applied to the RR interval of ECG to analyze the four classes different degrees of sleep apnea.MEI1~3 and MEI13~15 are defined according to multiscale entropy index (MEI).The results of the study not only indicate that MEI1~3 and MEI13~15 can distinguish between normal sleep groups, mild sleep apnea patients, moderate and severe apnea patients, but also reveal good negative correlation between MEI1~3 and AHI (R2=0.279,P<0.05).