High resolution ECG filtering using adaptive bayesian wavelet shrinkage

M. Popescu, P. Cristea, A. Bezerianos

Research output: Contribution to journalArticlepeer-review

18 Scopus citations


This paper outlines a bayesian wavelet shrinkage denoising approach for High Resolution ECG (HRECG) filtering. The proposed filtering method comprises three basic steps: the dyadic Wavelet Transform (WT) computation, the shrinkage of the wavelet coefficients using adaptive bayesian rules, and the reconstruction of the denoised signal through the inverse WT. An automatic, level-dependent scheme is designed to estimate the shrinkage functions, using a maximum likelihood procedure across the WT coefficients from the ensemble of available beats. The performance evaluation using controlled simulation experiments revealed that the present technique outperforms the wavelet soft- and hard- thresholding methods in preserving the high-frequency components of the QRS complex.

Original languageEnglish (US)
Pages (from-to)401-404
Number of pages4
JournalComputers in cardiology
StatePublished - Jan 1 1998

ASJC Scopus subject areas

  • Computer Science Applications
  • Cardiology and Cardiovascular Medicine


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