ECG signal denoising using higher order statistics in Wavelet subbands
ECG signal denoising using higher order statistics in Wavelet subbands
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DOI：
10.1016/j.bspc.2010.03.003
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发表时间：
2010-07-01
影响因子：
5.1
通讯作者：
Mahanta, A.
中科院分区：
文献类型：
--
作者：
Sharma, L. N.;Dandapat, S.;Mahanta, A.
在这项工作中，我们提出了一种新的去噪方法的基础上评价高阶统计量在不同的小波频带的心电图（ECG）信号。不同小波频带的高阶统计量提供了有关时间和频率数据统计性质的重要信息。结合小波子带中信号的四阶累积量、峰度和能量贡献效率来评估信号中的噪声含量。因此，提出了四个去噪因子。去噪因子的性能进行了评估，并与软阈值方法进行了比较。滤波后的信号质量使用百分比均方根差（PRD）、小波加权百分比均方根差（WWPRD）和基于小波能量的诊断失真（WEDD）测量来评估。据观察，所提出的去噪方案不仅有效地过滤信号，但也有助于保留诊断信息。(C)2010爱思唯尔有限公司保留所有权利。
In this work, we propose a novel denoising method based on evaluation of higher-order statistics at different Wavelet bands for an electrocardiogram (ECG) signal. Higher-order statistics at different Wavelet bands provides significant information about the statistical nature of the data in time and frequency. The fourth order cumulant, Kurtosis, and the Energy Contribution Efficiency (ECE) of signal in a Wavelet subband are combined to assess the noise content in the signal. Accordingly, four denoising factors are proposed. Performance of the denoising factors is evaluated and compared with the soft thresholding method. The filtered signal quality is assessed using Percentage Root Mean Square Difference (PRD), Wavelet Weighted Percentage Root Mean Square Difference (WWPRD), and Wavelet Energy-based Diagnostic Distortion (WEDD) measures. It is observed that the proposed denoising scheme not only filters the signal effectively but also helps retain the diagnostic information. (C) 2010 Elsevier Ltd. All rights reserved.