Combination of Canonical Correlation Analysis and Empirical Mode Decomposition Applied to Denoising the Labor Electrohysterogram
Combination of Canonical Correlation Analysis and Empirical Mode Decomposition Applied to Denoising the Labor Electrohysterogram
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DOI：
10.1109/tbme.2011.2151861
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发表时间：
2011-09-01
影响因子：
4.6
通讯作者：
Marque, Catherine
中科院分区：
文献类型：
--
作者：
Hassan, Mahmoud;Boudaoud, Sofiane;Marque, Catherine
子宫电图（EHG）经常受到电子和电磁噪声以及运动伪影、骨骼肌肌电图和来自母亲和胎儿的ECG的破坏。干扰信号是零星的和/或具有与感兴趣的信号的频谱重叠的频谱，使得经典滤波无效。在缺乏有效的单极EHG信号去噪方法的情况下，通常使用双极方法。本文提出了一种新的结合典型相关分析（BSS_CCA）和经验模态分解（EMD）方法的盲源分离方法，对单极EHG信号进行去噪。我们首先使用BSS_CCA提取子宫爆发，然后使用EMD从爆发中去除任何残留噪声的最大部分。将该算法与小波滤波和独立分量分析进行了比较。我们还将CCA_EMD与相应的双极信号进行了比较，以证明新方法给出的信号没有被新方法降级。所提出的方法成功地从信号中去除了伪影，而不改变双极方法观察到的潜在子宫活动。CCA_EMD算法的性能明显优于比较方法。
The electrohysterogram (EHG) is often corrupted by electronic and electromagnetic noise as well as movement artifacts, skeletal electromyogram, and ECGs from both mother and fetus. The interfering signals are sporadic and/or have spectra overlapping the spectra of the signals of interest rendering classical filtering ineffective. In the absence of efficient methods for denoising the monopolar EHG signal, bipolar methods are usually used. In this paper, we propose a novel combination of blind source separation using canonical correlation analysis (BSS_CCA) and empirical mode decomposition (EMD) methods to denoise monopolar EHG. We first extract the uterine bursts by using BSS_CCA then the biggest part of any residual noise is removed from the bursts by EMD. Our algorithm, called CCA_EMD, was compared with wavelet filtering and independent component analysis. We also compared CCA_EMD with the corresponding bipolar signals to demonstrate that the new method gives signals that have not been degraded by the new method. The proposed method successfully removed artifacts from the signal without altering the underlying uterine activity as observed by bipolar methods. The CCA_EMD algorithm performed considerably better than the comparison methods.