Myopotential denoising of ECG signals using wavelet thresholding methods

Vladimir Cherkassky, Steven Kilts

Research output: Contribution to journalArticlepeer-review

29 Scopus citations

Abstract

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).

Original languageEnglish (US)
Pages (from-to)1129-1137
Number of pages9
JournalNeural Networks
Volume14
Issue number8
DOIs
StatePublished - Jan 1 2001

Keywords

  • Complexity control
  • ECG denoising
  • Model selection
  • Myopotential noise
  • Signal denoising
  • VC-theory
  • Wavelet thresholding

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