Empirical mode decomposition (EMD) is a novel algorithm developed recently. EMD is based on the decomposition derived from the data and is useful for the analysis on nonlinear and nonstationary time series signals. With iterative decomposition of signals, EMD separated the full signal into ordered elements that with frequency ranged from higher to lower frequencies on each intrinsic mode function (IMF) levels. The major disadvantage of EMD is mode mixing effect. Mode mixing indicates that oscillations of different time scales coexist in a given IMF, or that oscillations with the same time scale have been assigned to different IMFs. Hence, ensemble EMD (EEMD) was introduced to remove the mode-mixing effect. The principle of the EEMD is to add white noise into the signal with many trials. Noise in each trial is different, and the added noise can be canceled out on an average, if the trial number is sufficient. Thus, the residual part is the signal, as more and more trials are added in the ensemble. In this study, a novel noise filtering algorithm is proposed based on ensemble empirical mode decomposition (EEMD) to remove artifacts within ECG. Three noise patterns, 50 Hz, EMG, and base line wander with different power were embedded into the simulated ECG signal. Traditional IIR filter, Wiener filter, empirical mode decomposition (EMD) and EEMD were used to compare filtering performance. Phase delay and the mean square error between clean simulated ECG and filtered ECG were used as filtering performance index. Results showed both nearly zero phase delay and high noise filtering are major advantage of EEMD based filter, especially on arrhythmia ECG.
