Open AccessComparative study of ECG signal denoising by wavelet thresholding in empirical and variational mode decomposition domains
Author(s) -
Lahmiri Salim
Publication year - 2014
Publication title -
healthcare technology letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.45
H-Index - 19
ISSN - 2053-3713
DOI - 10.1049/htl.2014.0073
Subject(s) - hilbert–huang transform , thresholding , noise reduction , wavelet , artificial intelligence , signal (programming language) , computer science , pattern recognition (psychology) , mode (computer interface) , decomposition , wavelet transform , signal to noise ratio (imaging) , algorithm , speech recognition , white noise , telecommunications , chemistry , image (mathematics) , organic chemistry , programming language , operating system
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.