Modified approach for ECG signal denoising based on empirical mode decomposition and moving average filter

Abstract

Electrocardiogram (ECG) signals are the reflections of the heart’s condition and hence any abnormal heart condition will also appear as irregularities in the ECG signal. Different noises that get embedded with ECG signal during its acquisition and transmission are powerline interference, baseline wandering, electromyogram noises, motion artefacts, and channel noises. Hence, for the proper diagnosis of the heart, the ECG signals must be free of noises. In this work, denoising of the ECG signal is the major objective and technique used for this purpose is based on the empirical mode decomposition (EMD) due to its adaptive and data driven nature suitable for any non-stationary signal. The high frequency noises have been considered for removal that includes electromyogram noises (MA), motion artefacts (EM), as well as White Gaussian noises. The proposed algorithm is compared with the existing algorithms and promising results are obtained which justifies the validation of the proposed method.