Noise And Artifacts Elimination In ECG Signals Using Wavelet, Variational Mode Decomposition And Nonlocal Means Algorithm

TL;DR

This paper presents two advanced preprocessing methods combining wavelet, variational mode decomposition, and nonlocal means to effectively eliminate noise and artifacts from ECG signals, improving signal clarity for better diagnosis.

Contribution

It introduces two novel ECG noise removal techniques that integrate wavelet and VMD with nonlocal means, enhancing artifact suppression over existing methods.

Findings

Effective removal of white Gaussian noise and baseline wander.

Improved ECG signal quality demonstrated in simulations.

First method uses wavelet analysis; second replaces it with VMD.

Abstract

Electrocardiogram (ECG) signals can frequently be affected by the introduction of noise and artifacts. Since these types of signal corruptions disrupt the accurate interpretation of ECG signals, noise and artifacts must be eliminated during the preprocessing phase. In this paper, we introduced a comprehensive pre-processing phase that eliminates motion artifacts and noise prior to detecting and extracting entirely corrupted ECG signal segments. The first method, denoted as the WLNH method, is constructed using wavelet multiresolution analysis (MRA), the Lillifors test, NLM, and a high-pass filter. The second method entails substituting the wavelet MRA decomposition with the variational mode decomposition (VMD) while retaining all other stages from the first method. This technique is denoted as the VLWNH. The two proposed methods differ from some existing methods in that they first employ the Lilliefors test to identify whether a component is white Gaussian noise and then utilize the High Pass Filter to eliminate motion anomalies. The simulation results show that the offered solutions are effective, particularly when dealing with white Gaussian noise and base-line wander (BW) noise.