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 language||English (US)|
|Number of pages||4|
|Journal||Computers in cardiology|
|State||Published - Jan 1 1998|
ASJC Scopus subject areas
- Computer Science Applications
- Cardiology and Cardiovascular Medicine