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An Efficient ECG Denoising Method Based on Empirical Mode Decomposition, Sample Entropy, and Improved Threshold Function
Wireless Communications and Mobile Computing Pub Date : 2020-12-22 , DOI: 10.1155/2020/8811962
Dengyong Zhang 1, 2 , Shanshan Wang 1, 2 , Feng Li 1, 2 , Shang Tian 1, 2 , Jin Wang 1, 2 , Xiangling Ding 3 , Rongrong Gong 4
Affiliation  

The electrocardiogram (ECG) signal can easily be affected by various types of noises while being recorded, which decreases the accuracy of subsequent diagnosis. Therefore, the efficient denoising of ECG signals has become an important research topic. In the paper, we proposed an efficient ECG denoising approach based on empirical mode decomposition (EMD), sample entropy, and improved threshold function. This method can better remove the noise of ECG signals and provide better diagnosis service for the computer-based automatic medical system. The proposed work includes three stages of analysis: (1) EMD is used to decompose the signal into finite intrinsic mode functions (IMFs), and according to the sample entropy of each order of IMF following EMD, the order of IMFs denoised is determined; (2) the new threshold function is adopted to denoise these IMFs after the order of IMFs denoised is determined; and (3) the signal is reconstructed and smoothed. The proposed method solves the shortcoming of discarding the first-order IMF directly in traditional EMD denoising and proposes a new threshold denoising function to improve the traditional soft and hard threshold functions. We further conduct simulation experiments of ECG signals from the MIT-BIH database, in which three types of noise are simulated: white Gaussian noise, electromyogram (EMG), and power line interference. The experimental results show that the proposed method is robust to a variety of noise types. Moreover, we analyze the effectiveness of the proposed method under different input SNR with reference to improving SNR () and mean square error (), then compare the denoising algorithm proposed in this paper with previous ECG signal denoising techniques. The results demonstrate that the proposed method has a higher and a lower . Qualitative and quantitative studies demonstrate that the proposed algorithm is a good ECG signal denoising method.

中文翻译:

基于经验模态分解,样本熵和改进阈值函数的高效ECG去噪方法

心电图(ECG)信号在记录时容易受到各种噪声的影响,从而降低了后续诊断的准确性。因此,心电信号的有效去噪已成为重要的研究课题。在本文中,我们提出了一种基于经验模式分解(EMD),样本熵和改进的阈值函数的有效ECG去噪方法。这种方法可以更好地消除心电信号的噪声,并为基于计算机的自动医疗系统提供更好的诊断服务。拟议的工作包括三个阶段的分析:(1)EMD用于将信号分解为有限本征函数(IMF),并根据EMD之后的IMF各个阶的样本熵,确定去噪后的IMF阶;(2)在确定去噪的IMF的阶数之后,采用新的阈值函数对这些IMF去噪;(3)重构并平滑信号。该方法解决了传统EMD去噪中直接丢弃一阶IMF的缺点,并提出了一种新的阈值去噪功能,以改进传统的软阈值和硬阈值功能。我们进一步进行了来自MIT-BIH数据库的ECG信号的模拟实验,其中模拟了三种类型的噪声:高斯白噪声,肌电图(EMG)和电源线干扰。实验结果表明,该方法对多种噪声类型均具有鲁棒性。此外,我们在改善输入信噪比的同时,分析了在不同输入信噪比下该方法的有效性 (3)重构并平滑信号。该方法解决了传统EMD去噪中直接丢弃一阶IMF的缺点,并提出了一种新的阈值去噪功能,以改进传统的软阈值和硬阈值功能。我们进一步进行了来自MIT-BIH数据库的ECG信号的模拟实验,其中模拟了三种类型的噪声:高斯白噪声,肌电图(EMG)和电源线干扰。实验结果表明,该方法对多种噪声类型均具有鲁棒性。此外,我们在改善输入信噪比的基础上,分析了该方法在不同输入信噪比下的有效性。(3)重构并平滑信号。该方法解决了传统EMD去噪中直接丢弃一阶IMF的缺点,并提出了一种新的阈值去噪功能,以改进传统的软阈值和硬阈值功能。我们进一步进行了来自MIT-BIH数据库的ECG信号的模拟实验,其中模拟了三种类型的噪声:高斯白噪声,肌电图(EMG)和电源线干扰。实验结果表明,该方法对多种噪声类型均具有鲁棒性。此外,我们在改善输入信噪比的基础上,分析了该方法在不同输入信噪比下的有效性。该方法解决了传统EMD去噪中直接丢弃一阶IMF的缺点,并提出了一种新的阈值去噪功能,以改进传统的软阈值和硬阈值功能。我们进一步进行了来自MIT-BIH数据库的ECG信号的模拟实验,其中模拟了三种类型的噪声:高斯白噪声,肌电图(EMG)和电源线干扰。实验结果表明,该方法对多种噪声类型均具有鲁棒性。此外,我们在改善输入信噪比的基础上,分析了该方法在不同输入信噪比下的有效性。该方法解决了传统EMD去噪中直接丢弃一阶IMF的缺点,并提出了一种新的阈值去噪功能,以改善传统的软阈值和硬阈值功能。我们进一步进行了来自MIT-BIH数据库的ECG信号的模拟实验,其中模拟了三种类型的噪声:高斯白噪声,肌电图(EMG)和电源线干扰。实验结果表明,该方法对多种噪声类型均具有鲁棒性。此外,我们在改善输入信噪比的基础上,分析了该方法在不同输入信噪比下的有效性。其中模拟了三种类型的噪声:高斯白噪声,肌电图(EMG)和电源线干扰。实验结果表明,该方法对多种噪声类型均具有鲁棒性。此外,我们在改善输入信噪比的基础上,分析了该方法在不同输入信噪比下的有效性。其中模拟了三种类型的噪声:高斯白噪声,肌电图(EMG)和电源线干扰。实验结果表明,该方法对多种噪声类型均具有鲁棒性。此外,我们在改善输入信噪比的基础上,分析了该方法在不同输入信噪比下的有效性。和均方误差(),然后将本文提出的降噪算法与以前的ECG信号降噪技术进行比较。结果表明,该方法具有较高和较低的精度定性和定量研究表明,该算法是一种很好的心电信号去噪方法。
更新日期:2020-12-22
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