Fuzzy multiwavelet denoising on ECG signal

Since different multiwavelets, pre- and post-filters, have different impulse and frequency response 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). Some fuzzy rules on selecting and integrating different multiwavelets, pre- and post-filters together, are proposed. These fuzzy rules are set up 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, it was found that the proposed fuzzy rule-based multiwavelet denoising algorithm achieves 30% improvement compared to traditional multiwavelet denoising algorithms.