Processing of impedance cardiogram differential for non-invasive cardiac function detection_Journal of Biomedical Engineering

Authors：

ZHANG Yadan ¹ ,  JI Zhong ^1,2 , TAN Xia ¹ , SHEN Zhe ³ , XU Lianjiao ⁴

1. College of biological Engineering, Chongqing University, Chongqing 400044, P.R.China;
2. Chongqing Medical Electronics Engineering Technology Center, Chongqing 400044, P.R.China;
3. Henan Province Medical Instrument Testing Institute, Zhengzhou 450008, P.R.China;
4. Chongqing Armed Corps Police Hospital, Chongqing 400061, P.R.China;

Corresponding author：

JI Zhong, Email: jizhong@cqu.edu.cn

Keywords：

adaptive ensemble empirical mode decomposition; impedance cardiogram differential; feature detection; preprocessing; non-invasive cardiac function detection

DOI：

10.7507/1001-5515.201804014

Video：

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Abstract Full text Figures/Tables Video References Cited by

The precise recognition of feature points of impedance cardiogram (ICG) is the precondition of calculating hemodynamic parameters based on thoracic bioimpedance. To improve the accuracy of detecting feature points of ICG signals, a new method was proposed to de-noise ICG signal based on the adaptive ensemble empirical mode decomposition and wavelet threshold firstly, and then on the basis of adaptive ensemble empirical mode decomposition, we combined difference and adaptive segmentation to detect the feature points, A, B, C and X, in ICG signal. We selected randomly 30 ICG signals in different forms from diverse cardiac patients to examine the accuracy of the proposed approach and the accuracy rate of the proposed algorithm is 99.72%. The improved accuracy rate of feature detection can help to get more accurate cardiac hemodynamic parameters on the basis of thoracic bioimpedance.

Citation： ZHANG Yadan, JI Zhong, TAN Xia, SHEN Zhe, XU Lianjiao. Processing of impedance cardiogram differential for non-invasive cardiac function detection. Journal of Biomedical Engineering, 2019, 36(1): 50-58. doi: 10.7507/1001-5515.201804014 Copy

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3.	Naidu S M M, Pandey P C, Bagal U R, et al. Beat-to-beat estimation of stroke volume using impedance cardiography and artificial neural network. Med Biol Eng Comput, 2018, 56(6): 1077-1089.
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9.	Stepanov R, Podtaev S, Dumler A, et al. Assessment of cardiac time intervals by wavelet transform of the impedance cardiogram. Technology and Health Care, 2016, 24(2): S803-S809.
10.	Janusauskas A, Marozas V, Lukosevicius A. Ensemble empirical mode decomposition based feature enhancement of cardio signals. Med Eng Phys, 2013, 35(8): 1059-1069.
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12.	Choudhari P C, Panse D S. Denoising of radial bioimpedance signals using adaptive wavelet packet transform and kalman filter. IOSR J VLSI Signal Process(IOSR-JVSP), 2015, 5(1): 01-08.
13.	刘珊. 基于小波变换的心电和心阻抗信号的研究. 太原: 太原理工大学, 2014.
14.	赵越. 心阻抗检测及其信号分析方法研究. 沈阳: 沈阳工业大学, 2015.
15.	Flandrin P, Rilling G, Concalves P. Empirical mode decomposition as a filter band. IEEE Signal Process Lett, 2004, 11(2): 112-114.
16.	秦树人, 季忠, 尹爱军. 工程信号处理. 北京: 高等教育出版社, 2008: 430-438.
17.	Rani V A, Tirumalareddy B, Babu C H. ECG signal denoising using EEMD and adaptive filter. Res J Pharm Biol Chem Sci, 2016, 7(4): 2734-2741.
18.	Chen Y, Zhou C, Yuan J, et al. Application of empirical mode decomposition in random noise attenuation of seismic data. Journal of Seismic Exploration, 2014, 23(5): 481-495.
19.	孟繁林. 集合经验模态分解的理论及应用研究. 镇江: 江苏科技大学, 2013.
20.	Zhang Yadan, Ji Zhong, Tan Xia, et al. Noise reduction of the electrocardiography signal and thoracic impedance differential signal based on adaptive EEMD and wavelet thresholding. Journal of Medical Imaging and Health Informatics, 2018, 8(1): 140-144.
21.	霍威, 季忠, 赵云冬. 一种基于胸阻抗法的心功能无创检测分析仪. 北京生物医学工程, 2015, 34(5): 489-494.

1. Hu X, Chen X, Ren R, et al. Adaptive filtering and characteristics extraction for Impedance Cardiography. Journal of Fiber Bioengineering & Informatics, 2014, 7(1): 81-90.
2. Nederend I, ten Harkel A D J, Blom N A, et al. Impedance cardiography in healthy children and children with congenital heart disease: improving stroke volume assessment. International Journal of Psychophysiology, 2017, 120: 136-147.
3. Naidu S M M, Pandey P C, Bagal U R, et al. Beat-to-beat estimation of stroke volume using impedance cardiography and artificial neural network. Med Biol Eng Comput, 2018, 56(6): 1077-1089.
4. Árbol J R, Perakakis P, Garrido A, et al. Mathematical detection of aortic valve opening (B point) in impedance cardiography: a comparison of three popular algorithms. Psychophysiology, 2017, 54(3): 350-357.
5. Naidu S M M, Pandey P C, Pandey V K. Automatic detection of characteristic points in impedance cardiogram//2011 Computing In Cardiology, Hangzhou: Zhejiang University, 2011: 497-500.
6. 赵云冬, 季忠, 彭承琳, 等. 基于小波变换的心阻抗微分信号去噪及特征点检测研究. 生物医学工程学杂志, 2015, 32(2): 284-289.
7. Podtaev S, Stepanov R, Dumler A, et al. Wavelet analysis of the impedance cardiogram waveforms. J Phys Conf Ser, 2012, 407(1): 2003-2009.
8. Sangkum L, LIU Gl, YU L, et al. Minimally invasive or noninvasive cardiac output measurement: an update. J Anesth, 2016, 30(3): 461-480.
9. Stepanov R, Podtaev S, Dumler A, et al. Assessment of cardiac time intervals by wavelet transform of the impedance cardiogram. Technology and Health Care, 2016, 24(2): S803-S809.
10. Janusauskas A, Marozas V, Lukosevicius A. Ensemble empirical mode decomposition based feature enhancement of cardio signals. Med Eng Phys, 2013, 35(8): 1059-1069.
11. Chabchoub S, Mansouri S, Ben Salah R. Impedance cardiography signal denoising using discrete wavelet transform. Australasian Physical & Engineering Sciences in Medicine, 2016, 39(3): 655-663.
12. Choudhari P C, Panse D S. Denoising of radial bioimpedance signals using adaptive wavelet packet transform and kalman filter. IOSR J VLSI Signal Process(IOSR-JVSP), 2015, 5(1): 01-08.
13. 刘珊. 基于小波变换的心电和心阻抗信号的研究. 太原: 太原理工大学, 2014.
14. 赵越. 心阻抗检测及其信号分析方法研究. 沈阳: 沈阳工业大学, 2015.
15. Flandrin P, Rilling G, Concalves P. Empirical mode decomposition as a filter band. IEEE Signal Process Lett, 2004, 11(2): 112-114.
16. 秦树人, 季忠, 尹爱军. 工程信号处理. 北京: 高等教育出版社, 2008: 430-438.
17. Rani V A, Tirumalareddy B, Babu C H. ECG signal denoising using EEMD and adaptive filter. Res J Pharm Biol Chem Sci, 2016, 7(4): 2734-2741.
18. Chen Y, Zhou C, Yuan J, et al. Application of empirical mode decomposition in random noise attenuation of seismic data. Journal of Seismic Exploration, 2014, 23(5): 481-495.
19. 孟繁林. 集合经验模态分解的理论及应用研究. 镇江: 江苏科技大学, 2013.
20. Zhang Yadan, Ji Zhong, Tan Xia, et al. Noise reduction of the electrocardiography signal and thoracic impedance differential signal based on adaptive EEMD and wavelet thresholding. Journal of Medical Imaging and Health Informatics, 2018, 8(1): 140-144.
21. 霍威, 季忠, 赵云冬. 一种基于胸阻抗法的心功能无创检测分析仪. 北京生物医学工程, 2015, 34(5): 489-494.

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