电子测量与仪器学报

付林军,王凤随,刘正男.改进自适应 CEEMD 方法在心电信号去噪中的应用[J].电子测量与仪器学报,2020,34(4):50-57

改进自适应 CEEMD 方法在心电信号去噪中的应用

Application of improved adaptive CEEMD method in denoising of ECG signals

DOI：

英文关键词:ECG signal adaptive complementary set empirical mode decomposition signal to noise ratio

基金项目:安徽省自然科学基金(1708085MF154)、安徽高校省级自然科学研究基金(KJ2019A0162,KJ2015A071)资助项目

作者	单位
付林军	1. 高端装备先进感知与智能控制教育部重点实验室,2. 安徽工程大学电气工程学院
王凤随	1. 高端装备先进感知与智能控制教育部重点实验室,2. 安徽工程大学电气工程学院
刘正男	1. 高端装备先进感知与智能控制教育部重点实验室,2. 安徽工程大学电气工程学院

Author	Institution
Fu Linjun	1. Key Laboratory of Advanced Perception and Intelligent Control of High-end Equipment, Ministry of Education,2. Anhui Polytechnic University College of Electrical Engineering
Wang Fengsui	1. Key Laboratory of Advanced Perception and Intelligent Control of High-end Equipment, Ministry of Education,2. Anhui Polytechnic University College of Electrical Engineering
Liu Zhengnan	1. Key Laboratory of Advanced Perception and Intelligent Control of High-end Equipment, Ministry of Education,2. Anhui Polytechnic University College of Electrical Engineering

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中文摘要:

针对传统经验模式分解(EMD)方法存在的模式混淆问题,以及总体平均经验模式分解(EEMD)不具备完备性和计算量太大的缺陷,提出一种改进的自适应互补集合经验模式分解(CEEMD)方法。该方法在分析加噪准则的基础上,引入峰值误差 (PE)作为加噪评价指标,来自适应确定最佳加噪幅值;然后利用原始信号的幅值标准差以及加入噪声的幅值标准差的比值系数,对不同信号自适应获取总体平均次数;最后将该方法运用到由美国麻省理工学院建立的 MIT-BIH 心电数据库中,很好地实现了对目标信号的去噪。实验表明,所提方法的平均信噪比(SNR)达到了 19. 249 7、均方根误差(RMSE)仅为 0. 047 3,平均平滑度指标 R 只有 0. 030 5。算法有效地去除了原始心电信号噪声,改善了信号的平滑度,提高了运算效率。

英文摘要:

Aiming at the problem of pattern confusion in traditional empirical mode decomposition (EMD) method and the fact that the overall mean empirical mode decomposition (EEMD) does not have completeness and computational complexity, an improved adaptive complementary set empirical mode decomposition - (CEEMD) method is proposed. Based on the analysis of the noise adding criterion, this method introduces peak error (PE) as the noise adding evaluation index to adaptively determine the optimal noise adding amplitude. Then, the original signal amplitude standard deviation and the noise added amplitude standard deviation are used. The ratio coefficient is used to adaptively obtain the overall average number of times for different signals. Finally, the method is applied to the MIT-BIH ECG database established by the Massachusetts Institute of Technology, and the denoising of the target signal is well completed. Experiments show that the average SNR of the proposed method reaches 19. 249 7, the RMSE is only 0. 047 3, and the average smoothness index R is only 0. 030 5. The algorithm effectively removes the original ECG signal noise, improves the signal smoothness and improves the calculation efficiency.

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