Electrocardiogram signal denoising using non-local wavelet transform domain filtering

TL;DR

This paper introduces a novel nonlocal wavelet transform domain filtering method for ECG signal denoising, effectively removing additive white Gaussian noise by exploiting correlations among local and nonlocal samples, leading to improved diagnostic signal quality.

Contribution

It proposes a new ECG denoising technique that leverages nonlocal sample correlations and wavelet shrinkage, outperforming existing methods in noise reduction.

Findings

Significant improvement in denoising quality over existing methods

Effective removal of AWGN from wireless ECG recordings

Enhanced preservation of diagnostic features in ECG signals

Abstract

ECG signals are usually corrupted by baseline wander, power-line interference, muscle noise, etc. and numerous methods have been proposed to remove these noises. However, in case of wireless recording of the ECG signal it gets corrupted by the additive white Gaussian noise (AWGN). For the correct diagnosis, removal of AWGN from ECG signals becomes necessary as it affects the all the diagnostic features. The natural signals exhibit correlation among their samples and this property has been exploited in various signal restoration tasks. Motivated by that, in this work we propose a nonlocal wavelet transform domain ECG signal denoising method which exploits the correlations among both local and nonlocal samples of the signal. In the proposed method, the similar blocks of the samples are grouped in a matrix and then denoising is achieved by the shrinkage of its two-dimensional discrete wavelet transform coefficients. The experiments performed on a number of ECG signals show significant quantitative and qualitative improvement in denoising performance over the existing ECG signal denoising methods.