ECG denoising on bivariate shrinkage function exploiting interscale dependency of wavelet coefficients

This paper presents a new method for electrocardiogram (ECG) denoising based on bivariate shrinkage functions exploiting the interscale dependency of wavelet coefficients. Most nonlinear thresholding methods based on wavelet transform denoising assume that the wavelet coefficients are independent. However, wavelet coefficients of ECG signals have significant dependencies. In this paper, we proposed a new method by considering the dependencies between the coefficients and their parents in detail on a bivariate shrinkage function for denoising of an ECG signal corrupted by different types of noises, such as muscle artifact noise, electrode motion, and white noise. In real-time applications, reduction of computational time is very crucial, so we constructed the wavelet transform by lifting scheme. The lifting scheme is a new technique to construct wavelet transform; namely, second generation wavelet transform is an alternative and faster algorithm for a classical wavelet transform. The overall denoising performance of our proposed method was considered in relation to several measuring parameters, including types of wavelet filters (Daubechies 4 (DB4), Daubechies 6 (DB6), and Daubechies 8 (DB8)) and decomposition depth. Global performance was evaluated by means of the signal-to-noise ratio (SNR) and visual inspection. We used a set of MIT-BIH arrhythmia database ECG records. To evaluate our method, a comparative study was carried out that referred to effective data-driven techniques in the literature, namely VisuShrink, SureShrink, BayesShrink, and level-dependent threshold estimation. The experimental results indicated that the proposed methods in the paper were better than the compared methods in terms of retaining the geometrical characteristics of the ECG signal, SNR. Due to its simplicity and its fast implementation, the method can easily be used in clinical medicine.