基于EEMD的脉率变异性时频域研究  

Time-frequency Research of Pulse Rate Variability Based on the EEMD

作　　者：张根选[1] 石波[1] 张赛[1] 曹阳 

机构地区：[1]蚌埠医学院医学影像学系,蚌埠市233030 [2]深圳迪美泰数字医学技术有限公司,深圳市518067

出　　处：《中国医疗器械杂志》2017年第4期251-254,共4页Chinese Journal of Medical Instrumentation

基　　金：安徽省高等学校自然科学研究一般项目(KJ2015B062by)

摘　　要：该文基于集合经验模态分解(EEMD)具有非常适合分析非平稳、非线性信号的特点,将脉率变异性(PRV)信号通过EEMD分解成多个本征模态(IMF)分量。分别计算出每个IMF分量的能量,并根据各个IMF分量频谱的特点,重新对IMF分量的能量重构得到PRV的高频能量(EHF)、低频能量(ELF)、极低频能量(EVLF)。并通过实验与AR功率谱估计方法进行了比较。结果表明两种方法得到的PRV各个对应的频域参数相关系数>0.96,说明该方法可以真实地反映交感神经和迷走神经活动。In this paper, based on the feature of ensemble empirical mode decomposition（EEMD） which is very suitable for analyzing non-stationary and nonlinear signals, the pulse rate variability （PRV） signal is decomposed into multiple intrinsic mode function （IMF） components by EEMD, to calculate the energy of each IMF component respectively, and high-frequency energy（EHF）, low-frequency energy（ELF） and very low-frequency energy（EVLF） of PRV were reconstructed from the energy of the IMF components according to the characteristics of each IMF component spectrum. This method is compared with the traditional AR power spectrum estimation method through the experiment. The results show that the correlation coefficient of each frequency domain parameter corresponding to PRV obtained by the two methods is higher than 0.96, which indicates that the method can truly reflect the sympathetic nerve and vagus nerve activity.

关 键 词：脉率变异性 心率变异性 集合经验模态分解 

分 类 号：R318.6[医药卫生—生物医学工程]