Fuzzy rule based multiwavelet ECG signal denoising
Fuzzy rule based multiwavelet ECG signal denoising
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DOI：
10.1109/fuzzy.2008.4630501
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发表时间：
2008-06
期刊：
影响因子：
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通讯作者：
B. Ling;C. Y. Ho;H. Lam;T. Wong;A. Chan;P. Tam
中科院分区：
文献类型：
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作者：
B. Ling;C. Y. Ho;H. Lam;T. Wong;A. Chan;P. Tam
由于不同的多小波、前置和后置滤波器具有不同的脉冲响应和频率响应，如果信号受到加性高斯白噪声的干扰，则应选择不同的多小波和前置和后置滤波器，并在不同的噪声水平下进行信号去噪。本文提出了将不同的多小波、前置和后置滤波器集成在一起的模糊规则，以利用在不同噪声水平下使用不同的多小波、前置和后置滤波器对去噪性能的影响的专家知识。当接收到心电信号时，首先估计噪声水平。然后，基于估计的噪声水平和我们提出的模糊规则，将不同的多小波、前置和后置滤波器集成在一起。对多小波系数进行硬阈值处理。通过大量的计算机数值模拟，我们提出的基于模糊规则的多小波去噪算法比传统的多小波去噪算法提高了30%。
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%.