High resolution ECG filtering using adaptive bayesian wavelet shrinkage

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.