Electrocardiogram signals de-noising using lifting-based discrete wavelet transform

This paper introduces an effective technique for the denoising of electrocardiogram (ECG) signals corrupted by nonstationary noises. The technique is based on a second generation wavelet transform and level-dependent threshold estimator. Here, wavelet coefficients of ECG signals were obtained with lifting-based wavelet filters. A lifting scheme is used to construct second-generation wavelets and is an alternative and faster algorithm for a classical wavelet transform. The overall denoising performance of our proposed method is considered in relation to several measuring parameters, including types of wavelet filters (Haar, Daubechies 4 (DB4), Daubechies 6 (DB6), Filter(9-7), and Cubic B-splines), thresholding method, and decomposition depth. Three different kinds of noise were considered in this work: muscle artifact noise, electrode motion artifact noise, and white noise. Global performance is evaluated by means of the signal-to-noise ratio and visual inspection. Numerical results comparing the performance of the proposed method with that of nonlinear filtering techniques (median filter) are given. The results demonstrate consistently superior denoising performance of the proposed method over median filtering.