Adaptive denoising of ECG using EMD, EEMD and CEEMDAN signal processing techniques

Abstract Using adaptive signal processing techniques denoising of ECG signal is performed which is obtained from physionet database. In this paper, the baseline wandering noise is removed using different adaptive techniques such as Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD) and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN). All these algorithms are effectively used to decompose the noisy ECG signal into different Intrinsic Mode Functions (IMFs) and further these IMFs are filtered using low pass filtering method to extract the low frequency baseline component. The high frequency noise present in the reconstructed signal is reduced by further decomposing into IMFs using all the three methods. These IMFs are soft thresholded to remove the high frequency noise. The results obtained from the CEEMDAN outperform EMD and EEMD in extracting signal from noise. Further, distinct parameters such as skewnesscrest factor, RMS value and kurtosis are estimated for the reconstructed signal to analyse their behaviour.