Zhang Genxuan, Shi Bo, Zhang Sai, Tsau Young
Department of Medical Imaging, Bengbu Medical College, Bengbu, 233030.
Dimetek Digital Medical Technologies Ltd, Shenzhen, 518067.
Zhongguo Yi Liao Qi Xie Za Zhi. 2017 Jul 30;41(4):251-254. doi: 10.3969/j.issn.1671-7104.2017.04.005.
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.
本文基于非常适合分析非平稳和非线性信号的集合经验模态分解（EEMD）的特点，利用EEMD将心率变异性（PRV）信号分解为多个本征模态函数（IMF）分量，分别计算各IMF分量的能量，并根据各IMF分量频谱特征，由IMF分量能量重构PRV的高频能量（EHF）、低频能量（ELF）和极低频能量（EVLF）。通过实验将该方法与传统的AR功率谱估计方法进行比较。结果表明，两种方法得到的与PRV对应的各频域参数的相关系数均高于0.96，这表明该方法能够真实反映交感神经和迷走神经活动。
