Ling, Wing-Kuen and Ho, Charlotte Yuk-Fan and Wong, Pak-Lin and Chan, Albert Yick-Po and Tam, Peter Kwong-Shun (2003) Fuzzy multiwavelet denoising on an ECG signal. Electronics Letters, 39 (16). pp. 1163-1164. ISSN 1013-5194

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Abstract

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.

Item Type:	Article
Additional Information:	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.
Keywords:	fuzzy, multiwavelets, electrocardiogram, denoising
Subjects:	H Engineering > H610 Electronic Engineering
Divisions:	College of Sciences > Faculty of Science > Lincoln School of Engineering
Depositing User:	Wing-Kuen Ling
Date Deposited:	29 Jul 2010 10:00
Last Modified:	13 Mar 2013 08:43
URI:	http://eprints.lincoln.ac.uk/id/eprint/3067

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