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总体经验模态分解(EEMD)改进了经验模态分解(EMD)存在的模态混叠问题,依据信号自身的波动特点将信号分解,特别适合非线性非平稳信号的分析处理.ECG信号能量分布有一定的规律,疾病会引起能量分布的变化,研究ECG能量分布的改变对心脏疾病的研究和临床诊断有重要意义.本文将ECG信号通过EEMD方法分解为多个本征模态函数(IMF)分量,观察IMF分量的波动规律,指出了ECG信号在不同时间尺度上的波动特点和物理意义.将IMF分量分别计算能量,得到ECG的能量向量,并对健康人和三种心脏疾病患者能量向量进行对比分析.结果表明心脏疾病导致EEMD能量向量的高频分量显著降低,尤其是p1分量具有较好的区分度,可以作为心脏疾病诊断的参考依据.相比较传统的频域分析方法单纯关注频率而忽略信号自身特点和信号成分之间的相互作用,EEMD的分解结果依赖于ECG信号本身,因此更能够反映ECG信号的真实情况,揭示年龄和疾病对ECG能量分布的影响.
The general empirical mode decomposition (EEMD) improves the modal aliasing problem of empirical mode decomposition (EMD), and decomposes the signal according to the fluctuation characteristics of the signal itself, which is especially suitable for nonlinear non-stationary signal analysis and processing. There is a certain law, the disease will cause changes in energy distribution, the study of ECG energy distribution changes is of great significance for the study of heart disease and clinical diagnosis.In this paper, ECG signal is decomposed into several intrinsic mode function (IMF) by EEMD method, And observed the fluctuation law of the IMF components and pointed out the fluctuation characteristics and the physical meaning of the ECG signals on different time scales.The energy components of the IMF components were calculated respectively and the energy vectors of the ECG were obtained.The energy vectors of healthy people and three kinds of heart disease patients The results showed that the heart disease caused a significant decrease in the high frequency components of the EEMD energy vector, especially the p1 component has a good discrimination, which can be used as a reference for the diagnosis of heart disease.Compared with traditional frequency domain analysis method, While ignoring the interaction between signal characteristics and signal components, the decomposition of EEMD depends on the ECG signal itself, and therefore more Enough to reflect the real situation of the ECG signal, to reveal the effects of age and disease on the ECG energy distribution.