Fuzzy rule based multiwavelet ECG signal denoising

Ling, Wing-Kuen and Ho, Yuk-Fan and Lam, Hak-Keung and Wong, Pak-Lin and Chan, Yick-Po and Tam, Kwong-Shun (2008) Fuzzy rule based multiwavelet ECG signal denoising. In: Fuzzy Systems, 2008. FUZZ-IEEE 2008. (IEEE World Congress on Computational Intelligence). IEEE International Conference on , 1-6 June 2008, Hong Kong.

Documents
conference_multimedia_fuzzy_multiwavelet.pdf
[img]
[Download]
[img]
Preview
PDF
conference_multimedia_fuzzy_multiwavelet.pdf - Whole Document

212kB

Official URL: http://dx.doi.org/10.1109/FUZZY.2008.4630501

Abstract

Since different multiwavelets, pre- and post-filters have different impulse responses and frequency responses, different multiwavelets, pre- and post-filters should be selected and applied at different noise levels for signal denoising if signals are corrupted by additive white Gaussian noises. In this paper, some fuzzy rules are formulated for integrating different multiwavelets, pre- and post-filters together so that expert knowledge on employing different multiwavelets, pre- and post-filters at different noise levels on denoising performances is exploited. When an ECG signal is received, the noise level is first estimated. Then, based on the estimated noise level and our proposed fuzzy rules, different multiwavelets, pre- and post-filters are integrated together. A hard thresholding is applied on the multiwavelet coefficients. According to extensive numerical computer simulations, our proposed fuzzy rule based multiwavelet denoising algorithm outperforms traditional multiwavelet denoising algorithms by 30%.

Item Type:Conference or Workshop Item (Paper)
Additional Information:Since different multiwavelets, pre- and post-filters have different impulse responses and frequency responses, different multiwavelets, pre- and post-filters should be selected and applied at different noise levels for signal denoising if signals are corrupted by additive white Gaussian noises. In this paper, some fuzzy rules are formulated for integrating different multiwavelets, pre- and post-filters together so that expert knowledge on employing different multiwavelets, pre- and post-filters at different noise levels on denoising performances is exploited. When an ECG signal is received, the noise level is first estimated. Then, based on the estimated noise level and our proposed fuzzy rules, different multiwavelets, pre- and post-filters are integrated together. A hard thresholding is applied on the multiwavelet coefficients. According to extensive numerical computer simulations, our proposed fuzzy rule based multiwavelet denoising algorithm outperforms traditional multiwavelet denoising algorithms by 30%.
Keywords:fuzzy, multiwavelet denoising, ECG signal, wavelet denoising
Subjects:H Engineering > H610 Electronic Engineering
Divisions:College of Science > School of Engineering
ID Code:3125
Deposited By:INVALID USER
Deposited On:01 Aug 2010 21:00
Last Modified:13 Mar 2013 08:43

Repository Staff Only: item control page