Electrocardiogram signal denoising using non-local wavelet transform domain filtering

• Published in: IET signal processing Vol. 9; no. 1; pp. 88 - 96
• Main Authors: Yadav, Santosh Kumar, Sinha, Rohit, Bora, Prabin Kumar
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 01.02.2015
• Subjects: 2D discrete wavelet transform coefficients; additive white Gaussian noise; AWGN; AWGN removal; baseline wander; correlation matrix; correlation theory; Diagnostic systems; discrete wavelet transforms; ECG signal denoising method; electrocardiogram signal denoising; electrocardiography; Filtering; filtering theory; interference suppression; matrix algebra; medical signal processing; muscle noise; natural signals; Noise; Noise reduction; nonlocal wavelet transform domain filtering; power line interference; Recording; Restoration; Shrinkage; signal restoration; Wavelet transforms; wireless recording; correlation theory; ECG signal denoising method; electrocardiogram signal denoising; interference suppression; AWGN; muscle noise; discrete wavelet transforms; filtering theory; correlation matrix; AWGN removal; power line interference; electrocardiography; matrix algebra; medical signal processing; signal restoration; 2D discrete wavelet transform coefficients; additive white Gaussian noise; wireless recording; natural signals; nonlocal wavelet transform domain filtering; baseline wander
• ISSN: 1751-9675; 1751-9683; 1751-9683
• DOI: 10.1049/iet-spr.2014.0005

Electrocardiogram (ECG) signals are usually corrupted by baseline wander, power-line interference, muscle noise etc. 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 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 study we propose a non-local wavelet transform domain ECG signal denoising method which exploits the correlations among both local and non-local 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 improvements in denoising performance over the existing ECG signal denoising methods.