Significance of modified empirical mode decomposition for ECG denoising

The primary objective of the presented work is to exploit the power of modified empirical mode decomposition (M-EMD) for the denoising of ECG signals. It is well known that the ECG signals get corrupted by a number of noises during the recording process. Especially, during wireless ECG recording and ambulatory patient monitoring, the signal gets corrupted by additive white Gaussian noise (AWGN). Over the years, several techniques have been proposed for ECG denoising. Among those, empirical mode decomposition (EMD) and nonlocal means (NLM) algorithm are noted to be quite effective. Further, the NLM-based approach is better in retaining the morphological characteristics in comparison to the EMD. Consequently, the two approaches are effectively combined in this paper so that each one complements the other. In the proposed approach, the noisy ECG signal is first preprocessed using the NLM algorithm. This is followed by decomposition of the partially denoised output through M-EMD. The decomposed components are suitably thresholded and then reconstructed to obtain the final denoised signal. This largely addresses the issue of under-averaged regions noted in the case of NLM-based denoising. Furthermore, the proposed approach is noted to be superior to the other existing techniques.