Key result
Fuzzy rule-based multiwavelet algorithms improve denoising performance ~30% vs traditional methods.
Why the study?
Different multiwavelets, pre- and post-filters have varying impulse and frequency responses, requiring selection and integration at different noise levels for ECG signals corrupted by additive white Gaussian noise.
Does a fuzzy rule-based multiwavelet denoising algorithm improve denoising performance on ECG signals compared to traditional algorithms?
Does a fuzzy rule-based multiwavelet denoising algorithm improve denoising performance on ECG signals compared to traditional algorithms?
Effect estimate: 30% improvement
A fuzzy rule-based multiwavelet denoising algorithm significantly improves ECG signal quality compared to traditional methods in simulation models.
Adaptive fuzzy rules may enhance AWGN denoising; leaves open rigorous benchmarking against existing methods.
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.
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Ho et al. (2003) studied ECG signal denoising. Fuzzy rule-based multiwavelet denoising algorithm vs. Traditional multiwavelet denoising algorithms was evaluated on Denoising performance (30% improvement). The proposed fuzzy rule-based multiwavelet denoising algorithm achieved a 30% improvement in denoising performance compared to traditional multiwavelet denoising algorithms.
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