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总体经验模态分解能量向量用于ECG能量分布的研究

曾彭 刘红星 宁新宝 庄建军 张兴敢

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总体经验模态分解能量向量用于ECG能量分布的研究

曾彭, 刘红星, 宁新宝, 庄建军, 张兴敢

ECG energy distribution analysis using ensemble empirical mode decomposition energy vector

Zeng Peng, Liu Hong-Xing, Ning Xin-Bao, Zhuang Jian-Jun, Zhang Xing-Gan
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  • 总体经验模态分解(EEMD)改进了经验模态分解(EMD)存在的模态混叠问题, 依据信号自身的波动特点将信号分解, 特别适合非线性非平稳信号的分析处理. ECG信号能量分布有一定的规律, 疾病会引起能量分布的变化, 研究ECG能量分布的改变对心脏疾病的研究和临床诊断有重要意义. 本文将ECG信号通过EEMD方法分解为多个本征模态函数(IMF)分量, 观察IMF分量的波动规律, 指出了ECG信号在不同时间尺度上的波动特点和物理意义. 将IMF分量分别计算能量, 得到ECG的能量向量, 并对健康人和三种心脏疾病患者能量向量进行对比分析. 结果表明心脏疾病导致EEMD能量向量的高频分量显著降低, 尤其是p1分量具有较好的区分度, 可以作为心脏疾病诊断的参考依据. 相比较传统的频域分析方法单纯关注频率而忽略信号自身特点和信号成分之间的相互作用, EEMD的分解结果依赖于ECG信号本身, 因此更能够反映ECG信号的真实情况, 揭示年龄和疾病对ECG能量分布的影响.
    Ensemble empirical mode decomposition (EEMD) method eliminates mode mixing phenomenon which is an inherent problem in empirical mode decomposition (EMD), and decomposes signals according to their intrinsic characteristics. It is suitable for analyzing nonlinear and non-stationary signals. Electrocardiogram (ECG) energy distribution exhibits a certain regularity which may vary with heart diseases. Researches on ECG energy distribution change are important for heart disease clinical diagnosis. In this paper, we use EEMD method to analyze ECG and find out how ECG energy distribution varies with age and heart diseases. We decompose the ECG signal into several intrinsic mode function (IMF) components by EEMD, and find that these IMFs can reveal the fluctuation rhythm and physical significance of ECG on different time scales. After IMFs have been decomposed, we calculate their energy and obtain an energy vector. By comparing the energy vectors among healthy young subjects, healthy old subjects, and three types of patients suffering from different heart diseases, we find that there is a significant decrease of high-frequency components of energy vector in heart disease patients as compared to healthy subjects, and a slight decrease of healthy old subjects as compared to healthy young subjects. T-test is performed to compare heart disease subjects with healthy subjects. Results show that there are significant differences between certain energy vector components, especially the first component p1 which could be used as heart disease auxiliary diagnosis. Compared to traditional frequency-domain analysis methods which simply concern about the frequency of a signal and ignore its own characteristics and interactions between signal components, EEMD method depends on ECG signal itself, therefore can reflect its real characteristics, and reveals the way how age and illness influence ECG energy distribution accurately.
    • 基金项目: 国家自然科学基金(批准号: 61271079)和江苏省高校优势学科建设工程资助项目资助的课题.
    • Funds: Project supported by the National Natural Science Foundation of China (Grant No.61271079) and the A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions, China (PAPD).
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    Strohmenger H U, Lindner K H, Brown C G 1997 Chest 111 584

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    Wang X, Peng Y 2006 Journal of Biomedical Engineering Research 25 122 (in Chinese) [王星, 彭屹 2006 生物医学工程研究 25 122]

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    Seena V, Jerrin Y 2014 2nd International Conference on Devices, Circuits and Systems Combiatore, India, March 6-8, 2014 p1

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    Bouaziz F, Boutana D, Benidir M 2014 IET Signal Process. 8 774

    [10]

    Banerjee S, Mitra M 2014 IEEE Transactions on Instrumentation and Measurement 63 326

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    Dong H S 2012 Ph. D. Dissertation (Lanzhou: Lanzhou University of Technology) (in Chinese) [董红生 2012 博士学位论文 (兰州: 兰州理工大学)]

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    Huang N E, Shen Z, Long S R, Wu M C, Shih H H, Zheng Q, Nai-Chyuan Y, Tung C C, Liu H H 1998 Proc. R. Soc. Lond. A 454 903

    [13]

    Chang K M, Liu S H 2011 Journal of Signal Processing Systems 64 249

    [14]

    Fu M J, Zhuang J J, Hou F Z, Zhan Q B, Shao Y, Ning X B 2010 Chin. Phys. B 19 592

    [15]

    Li H Q, Wang X F, Chen L, Li E B 2014 Circuits Syst Signal Process 33 1261

    [16]

    Wu Z, Huang N E 2009 Adv. Adapt. Data Anal. 01 1

    [17]

    Zhang R R, Ma S, Safak E, Hartzell S 2003 Journal of Engineering Mechanics 129 861

    [18]

    Shi Z Y, Jia M P 2012 Mechanical Engineering and Technology 1 61 (in Chinese) [石智云, 贾民平 2012 机械工程与技术 1 61]

    [19]

    Blanco-Velasco M, Weng B, Barner K E 2008 Computers in Biology and Medicine 38 1

    [20]

    Xue C F, Hou W, Zhao J H, Wang S G 2013 Acta Phys. Sin. 62 109203 (in Chinese) [薛春芳, 侯威, 赵俊虎, 王式功 2013 物理学报 62 109203]

    [21]

    Nathaniel E U, Beloff N, George N J 2013 Chin. Phys. B 22 84701

    [22]

    Upganlawar I V, Chowhan H 2014 International Journal of Computer Trends and Technology 11 166

    [23]

    Pang Y, Deng L, Lin J C, Li Z Y, Zhou Q N, Li G Q, Huang H W, Zhang Y, Wu W 2014 Acta Phys. Sin. 63 0987011 (in Chinese) [庞宇, 邓璐, 林金朝, 李章勇, 周前能, 李国权, 黄华伟, 张懿, 吴炜 2014 物理学报 63 098701]

    [24]

    Ning X B, Zhang D B, Yan W T, Chen Y, Wei T X 2002 Journal of Nanjing University (Natural Sciences) 38 7 (in Chinese) [宁新宝, 张道斌, 阎文泰, 陈颖, 魏太星 2002 南京大学学报: 自然科学版 38 7]

  • [1]

    Cao X W, Deng Q K 2001 Chinese Journal of Medical Physics 18 46 (in Chinese) [曹细武, 邓亲恺 2001 中国医学物理学杂志 18 46]

    [2]

    Yang X D, Ning X B, He A J, Du S D 2008 Acta Phys. Sin. 57 1514 (in Chinese) [杨小冬, 宁新宝, 何爱军, 都思丹 2008 物理学报 57 1514]

    [3]

    Hamprecht F A, Achleitner U, Krismer A C, Lindner K H, Wenzel V, Strohmenger H U, Thiel W, van Gunsteren W F, Amann A 2001 Resuscitation 50 287

    [4]

    Strohmenger H U, Lindner K H, Brown C G 1997 Chest 111 584

    [5]

    Bojarnejad M, Blake J, Bourke J P, Murray A, Langley P 2012 39th Conference on Computing in Cardiology Krakow, Poland, September 09-12, 2012 p713

    [6]

    Xie B, Yan B G, Lan Z K, Ma S W, Che X Y 2013 Progress in Modern Biomedicine 13 3756 (in Chinese) [谢斌, 严碧歌, 兰正康, 马世文, 车晓燕 2013 现代生物医学进展 13 3756]

    [7]

    Wang X, Peng Y 2006 Journal of Biomedical Engineering Research 25 122 (in Chinese) [王星, 彭屹 2006 生物医学工程研究 25 122]

    [8]

    Seena V, Jerrin Y 2014 2nd International Conference on Devices, Circuits and Systems Combiatore, India, March 6-8, 2014 p1

    [9]

    Bouaziz F, Boutana D, Benidir M 2014 IET Signal Process. 8 774

    [10]

    Banerjee S, Mitra M 2014 IEEE Transactions on Instrumentation and Measurement 63 326

    [11]

    Dong H S 2012 Ph. D. Dissertation (Lanzhou: Lanzhou University of Technology) (in Chinese) [董红生 2012 博士学位论文 (兰州: 兰州理工大学)]

    [12]

    Huang N E, Shen Z, Long S R, Wu M C, Shih H H, Zheng Q, Nai-Chyuan Y, Tung C C, Liu H H 1998 Proc. R. Soc. Lond. A 454 903

    [13]

    Chang K M, Liu S H 2011 Journal of Signal Processing Systems 64 249

    [14]

    Fu M J, Zhuang J J, Hou F Z, Zhan Q B, Shao Y, Ning X B 2010 Chin. Phys. B 19 592

    [15]

    Li H Q, Wang X F, Chen L, Li E B 2014 Circuits Syst Signal Process 33 1261

    [16]

    Wu Z, Huang N E 2009 Adv. Adapt. Data Anal. 01 1

    [17]

    Zhang R R, Ma S, Safak E, Hartzell S 2003 Journal of Engineering Mechanics 129 861

    [18]

    Shi Z Y, Jia M P 2012 Mechanical Engineering and Technology 1 61 (in Chinese) [石智云, 贾民平 2012 机械工程与技术 1 61]

    [19]

    Blanco-Velasco M, Weng B, Barner K E 2008 Computers in Biology and Medicine 38 1

    [20]

    Xue C F, Hou W, Zhao J H, Wang S G 2013 Acta Phys. Sin. 62 109203 (in Chinese) [薛春芳, 侯威, 赵俊虎, 王式功 2013 物理学报 62 109203]

    [21]

    Nathaniel E U, Beloff N, George N J 2013 Chin. Phys. B 22 84701

    [22]

    Upganlawar I V, Chowhan H 2014 International Journal of Computer Trends and Technology 11 166

    [23]

    Pang Y, Deng L, Lin J C, Li Z Y, Zhou Q N, Li G Q, Huang H W, Zhang Y, Wu W 2014 Acta Phys. Sin. 63 0987011 (in Chinese) [庞宇, 邓璐, 林金朝, 李章勇, 周前能, 李国权, 黄华伟, 张懿, 吴炜 2014 物理学报 63 098701]

    [24]

    Ning X B, Zhang D B, Yan W T, Chen Y, Wei T X 2002 Journal of Nanjing University (Natural Sciences) 38 7 (in Chinese) [宁新宝, 张道斌, 阎文泰, 陈颖, 魏太星 2002 南京大学学报: 自然科学版 38 7]

计量
  • 文章访问数:  1973
  • PDF下载量:  377
  • 被引次数: 0
出版历程
  • 收稿日期:  2014-08-09
  • 修回日期:  2014-11-09
  • 刊出日期:  2015-04-05

总体经验模态分解能量向量用于ECG能量分布的研究

  • 1. 南京大学电子科学与工程学院, 生物医学电子工程研究所, 南京 210093
    基金项目: 

    国家自然科学基金(批准号: 61271079)和江苏省高校优势学科建设工程资助项目资助的课题.

摘要: 总体经验模态分解(EEMD)改进了经验模态分解(EMD)存在的模态混叠问题, 依据信号自身的波动特点将信号分解, 特别适合非线性非平稳信号的分析处理. ECG信号能量分布有一定的规律, 疾病会引起能量分布的变化, 研究ECG能量分布的改变对心脏疾病的研究和临床诊断有重要意义. 本文将ECG信号通过EEMD方法分解为多个本征模态函数(IMF)分量, 观察IMF分量的波动规律, 指出了ECG信号在不同时间尺度上的波动特点和物理意义. 将IMF分量分别计算能量, 得到ECG的能量向量, 并对健康人和三种心脏疾病患者能量向量进行对比分析. 结果表明心脏疾病导致EEMD能量向量的高频分量显著降低, 尤其是p1分量具有较好的区分度, 可以作为心脏疾病诊断的参考依据. 相比较传统的频域分析方法单纯关注频率而忽略信号自身特点和信号成分之间的相互作用, EEMD的分解结果依赖于ECG信号本身, 因此更能够反映ECG信号的真实情况, 揭示年龄和疾病对ECG能量分布的影响.

English Abstract

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