Fuzzy multiwavelet denoising on ECG signal

Since different multiwavelets, pre- and post-filters have different impulse and frequency responses characteristics, different multiwavelets, pre- and post-filters should be selected, integrated and applied at different noise levels if a signal is corrupted by an additive white Gaussian noise (AWGN). In this letter, some fuzzy rules on selecting and integrating different multiwavelets, pre- and post-filters together are proposed. These fuzzy rules are setup based on the training results of the denoising performances of applying different multiwavelets, pre- and post-filters at different noise levels. When a new electrocardiogram (ECG) signal is applied, the appropriate multiwavelets, pre- and post-filters are selected and integrated based on fuzzy rules and the noise level of the signal. A hard thresholding is applied on the multiwavelet coefficients. According to an extensive simulation, we found that our proposed fuzzy rule-based multiwavelet denoising algorithm achieves 30% improvement compared to the traditional multiwavelet denoising algorithms.