Nonlinear Filtering in ECG Signal Denoising

This paper presents a non-linear filtering method based on the multiresolution analysis of the Discrete Wavelet Transform (DWT). The main idea is to use the time-frequency localization properties of the wavelet decomposition. The proposed algorithm is using an extra decomposition of the identified noise in order to reduce the correlation between the electrocardiogram (ECG) signal and noise. The linear denoising approach assumes that the noise can be found within certain scales, for example, at the finest scales when the coarsest scales are assumed to be noise-free. The non-linear thresholding approach involves discarding the details exceeding a certain limit. This approach assumes that every wavelet coefficient contains noise which is distributed over all scales. The non-linear filter thresholds the wavelet coefficients and subtracts the correlated noise. The used threshold depends on the noise level in each of the frequency bands associated to the wavelet decomposition. The proposed non-linear filter acts by thresholding the detail coefficients in a particular way, in order to eliminate the correlation between the noise and the signal. In this paper, in order to evaluate the proposed filtering method, signals from the MIT-BIH database have been used, and the filtering procedure was performed with added Gaussian noise. The proposed procedure was compared with ordinary wavelet transform and wavelet packet transform based denoising procedures, the followed parameters are the signal to noise ratio and the denoising error.