ECG energy distribution analysis using ensemble empirical mode decomposition energy vector

Abstract

Ensemble empirical mode decomposition (EEMD) method eliminates mode mixing phenomenon which is an inherent problem in empirical mode decomposition (EMD), and decomposes signals according to their intrinsic characteristics. It is suitable for analyzing nonlinear and non-stationary signals. Electrocardiogram (ECG) energy distribution exhibits a certain regularity which may vary with heart diseases. Researches on ECG energy distribution change are important for heart disease clinical diagnosis. In this paper, we use EEMD method to analyze ECG and find out how ECG energy distribution varies with age and heart diseases. We decompose the ECG signal into several intrinsic mode function (IMF) components by EEMD, and find that these IMFs can reveal the fluctuation rhythm and physical significance of ECG on different time scales. After IMFs have been decomposed, we calculate their energy and obtain an energy vector. By comparing the energy vectors among healthy young subjects, healthy old subjects, and three types of patients suffering from different heart diseases, we find that there is a significant decrease of high-frequency components of energy vector in heart disease patients as compared to healthy subjects, and a slight decrease of healthy old subjects as compared to healthy young subjects. T-test is performed to compare heart disease subjects with healthy subjects. Results show that there are significant differences between certain energy vector components, especially the first component p1 which could be used as heart disease auxiliary diagnosis. Compared to traditional frequency-domain analysis methods which simply concern about the frequency of a signal and ignore its own characteristics and interactions between signal components, EEMD method depends on ECG signal itself, therefore can reflect its real characteristics, and reveals the way how age and illness influence ECG energy distribution accurately.