title: Comparative study of ECG signal denoising by wavelet thresholding in empirical and variational mode decomposition domains.
abstract: 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.
authors.affiliation: Department of Computer Science , University of Quebec at Montreal , Montreal , H2X 3Y7 , Canada.
authors.forename: Salim
authors.initials: S
authors.lastname: Lahmiri
keywords: AWGN; DWT thresholding; ECG signals; additive Gaussian noise; discrete wavelet transform; discrete wavelet transforms; electrocardiogram signal denoising; electroencephalography; hybrid denoising models; in empirical mode decomposition domains; medical signal processing; signal denoising; variational mode decomposition domains; wavelet thresholding