Myopotential denoising of ECG signals using wavelet thresholding methods.
We present empirical comparisons of several wavelet-denoising methods applied to the problem of removing (denoising) myopotential noise from the observed noisy ECG signal. Namely, we compare the denoising accuracy and robustness of several wavelet thresholding methods (VISU, SURE and soft thresholding) and a new thresholding approach based on Vapnik-Chervonenkis (VC) learning theory. Our findings indicate that the VC-based wavelet approach is superior to the standard thresholding methods in that it achieves: Higher denoising accuracy (in terms of both MSE measure and visual quality) and more robust and compact representation of the denoised signal (i.e., it uses fewer wavelets).
Cherkassky
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Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis 55455, USA. cherkass@ece.umn.edu
Kilts
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D000200:Action Potentials / Q000502:physiology*; D016477:Artifacts; D001185:Artificial Intelligence; D004562:Electrocardiography / Q000379:methods*; D006321:Heart / Q000502:physiology*; D006801:Humans; D009119:Muscle Contraction / Q000502:physiology; D012815:Signal Processing, Computer-Assisted