Nonlinear wavelet and wavelet packet denoising of electrocardiogram signal

The performance of different wavelet- and wavelet packet-based methods for removing simulated noise was studied using an electrocardiogram (ECG) signal. A non-linear denoising approach was investigated by applying soft and hard thresholding methods, in which thresholds were chosen using four different methods. Coiflet wavelet and wavelet packet functions were used to build up the dyadic wavelet and optimized wavelet packet decompositions. This study involves the quantitative comparison of different denoising approaches by means of optimized error measures and visual inspection of the denoised ECG and the error signal. The localization of the denoising error within the cardiac cycle was studied by visual inspection of the denoised signal and extracting the error measures during the QRS-complex. The results showed that wavelet denoising approaches were generally more efficient than wavelet packet approaches in all cases, but with Heuristic Sure threshold selection rule as hard thresholding for white noises was used. Denoising errors tend to concentrate within the QRS-area when the wavelet approach was employed. Moreover, soft and hard non-linearities showed different balances in denoising the high-frequency parts of an ECG.