New approach of threshold estimation for denoising ECG signal using wavelet transform

Abstract

This paper presents a new method of threshold estimation for ECG signal denoising using wavelet decomposition. In this method, threshold is computed using the maximum and minimum wavelet coefficients at each level. Using this threshold and well known Hard thresholding process, the significant wavelet coefficients from each level are selected and denoised ECG signal is reconstructed with inverse wavelet transform. The performance of this method is compared with all well know wavelet shrinkage denoising methods with bior4.4 wavelet using root mean square error (RMSE) and signal to noise ratio (SNR) on MIT-BIH ECG database. The proposed threshold estimation is simple and faster compared to all existing threshold calculation methods namely VisuShrink, SureShrink, BayesShrink, and level-dependent threshold estimation and gives better SNR and RMSE. Proposed threshold estimation process decreases data sorting and storing resources allowing low-cost and faster implementation for portable biomedical devices.