Comparative study of ECG signal denoising by wavelet thresholding in empirical and variational mode decomposition domains

Healthcare Technology Letters

article

Salim Lahmiri¹ 

[1] Department of Computer Science, University of Quebec at Montreal

关键词: discrete wavelet transforms;    medical signal processing;    electroencephalography;    AWGN;    signal denoising;    electrocardiogram signal denoising;    wavelet thresholding;    in empirical mode decomposition domains;    variational mode decomposition domains;    hybrid denoising models;    discrete wavelet transform;    additive Gaussian noise;    ECG signals;    DWT thresholding;   

DOI  :  10.1049/htl.2014.0073

学科分类：肠胃与肝脏病学

来源: Wiley

【 摘 要 】

Hybrid denoising models based on combining empirical mode decomposition (EMD) and discrete wavelet transform (DWT) were found to be effective in removing additive Gaussian noise from electrocardiogram (ECG) signals. Recently, variational mode decomposition (VMD) has been proposed as a multiresolution technique that overcomes some of the limits of the EMD. Two ECG denoising approaches are compared. The first is based on denoising in the EMD domain by DWT thresholding, whereas the second is based on noise reduction in the VMD domain by DWT thresholding. Using signal-to-noise ratio and mean of squared errors as performance measures, simulation results show that the VMD-DWT approach outperforms the conventional EMD–DWT. In addition, a non-local means approach used as a reference technique provides better results than the VMD-DWT approach.

【 授权许可】

CC BY|CC BY-ND|CC BY-NC|CC BY-NC-ND