Comprehensive Noise and Artifact Removal from ECG Signals Using Wavelet, Variational Mode Decomposition and Nonlocal Means Algorithms

The presence of noise and artifacts often has a significant impact on electrocardiogram (ECG) data. Signal corruptions of this nature impact the precise interpretation of ECG signals, necessitating the removal of noise and artifacts during the preprocessing stage. The present paper provides a thorough pre-processing phase that removes motion distortions and noise before identifying and recovering entirely distorted ECG signal segments. The initial technique, referred to as the WLNH method, is created by employing Wavelet multiresolution analysis (MRA), the Lilliefors test, NLM, and a high pass filter. The second approach involves replacing the wavelet MRA decomposition with the variational mode decomposition (VMD), while keeping all other steps from the previous approach. The abbreviation VLWNH is used to represent this approach. The two proposed methods distinguish themselves from existing methods by initially employing the Lilliefors test to determine if a component exhibits white Gaussian noise characteristics, and subsequently use the High Pass Filter to remove any motion irregularities. The simulation results demonstrate the effectiveness of the proposed strategies, especially in addressing white Gaussian noise and baseline wander (BW) noise.