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

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.	趙云冬, 季忠, 彭承琳, 等. 基于小波變換的心阻抗微分信號去噪及特征點檢測研究. 生物醫學工程學雜志, 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.	劉珊. 基于小波變換的心電和心阻抗信號的研究. 太原: 太原理工大學, 2014.
14.	趙越. 心阻抗檢測及其信號分析方法研究. 沈陽: 沈陽工業大學, 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.	秦樹人, 季忠, 尹愛軍. 工程信號處理. 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. 集合經驗模態分解的理論及應用研究. 鎮江: 江蘇科技大學, 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, 趙云冬. 一種基于胸阻抗法的心功能無創檢測分析儀. 北京生物醫學工程, 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. 趙云冬, 季忠, 彭承琳, 等. 基于小波變換的心阻抗微分信號去噪及特征點檢測研究. 生物醫學工程學雜志, 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. 劉珊. 基于小波變換的心電和心阻抗信號的研究. 太原: 太原理工大學, 2014.
14. 趙越. 心阻抗檢測及其信號分析方法研究. 沈陽: 沈陽工業大學, 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. 秦樹人, 季忠, 尹愛軍. 工程信號處理. 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. 集合經驗模態分解的理論及應用研究. 鎮江: 江蘇科技大學, 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, 趙云冬. 一種基于胸阻抗法的心功能無創檢測分析儀. 北京生物醫學工程, 2015, 34(5): 489-494.

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