Ensemble Empirical Mode Decomposition and adaptive filtering for ECG signal enhancement

The morphologic analysis of electrocardiogram (ECG) signals, which are always contaminated by certain types of noise, is a very important standard for medical diagnosis of heart diseases and other pathological phenomena. In this paper a novel ECG enhancement method based on Ensemble Empirical Mode Decomposition (EEMD) and adaptive filtering is proposed to filter out Gaussian noise and contact noise contained in raw ECG signals. The reference signal of the adaptive filter is produced by the selective reconstruction of the decomposition results of EEMD. Real ECG signals from the MIT-BIH database are used to validate the performance of the proposed method. Conventional Empirical Mode Decomposition (EMD), EEMD, and EEMD-Adaptive (EEMDA) are tested to compare the filtering performance. The results of simulations show that ECG signals can be significantly enhanced by using the proposed method where the contact noise is eliminated while useful ECG features are kept. It is shown that the EEMDA method is better than other filtering methods in terms of filtering ECG noise.