Denoising ECG signal by complete EEMD adaptive noise

Abstract

The ECG signals reflect all the electrical activities of the heart and are subjected to different kinds of noise. Hence denoising these signals is quite an important issue since the noise can hinder accurate diagnosis and detection of cardiac diseases. In this study, a novel strategy is proposed, based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) to remove Power Line Interference (PLI), with the performance being analyzed as well. The ECG signal is showed to be nonstationary and nonlinear. Therefore it is firstly decomposed by CEEMDAN to Intrinsic Mode Functions (IMFs), in order for problems of non-linearity and Mode Mixing issues in EMD must be solved. Then the first IMF is applied as a reference to different implementations of adaptive filters. Thus, solving the non-stationarity. Furthermore, by summing up the residual of the IMFs components, the first IMF is removed and the denoised signal constructed. The performance is compared with recent methods based on EEMD and measured by the Signal to Noise Ratio (SNR), correlation coefficient (CCR) and the Mean Square Error (MSE). Clean ECG records downloaded from the MIT-BIH Arrhythmia Database and the PLI noise are added and then denoised. It was founded that the SNR increased from 14.95241 to 27.295215 and the CCR increased from 0.984384 to 0.9991233 while MSE decreased from 0.141386 to 0.0341404, respectively for PLI noise. Results show that the proposed algorithm performs better than a recently developed algorithm.