Research on motion impedance cardiography de-noising method based on two-step spectral ensemble empirical mode decomposition and canonical correlation analysis_Journal of Biomedical Engineering

Authors：

 XIE Yao ^1,2 , YANG Dong ^1,2 , YU Honglong ² , XIE Qilian ^2,3

1. School of Information Science and Technology, University of Science and Technology of China, Hefei 230022, P. R. China;
2. Anhui Tongling Bionic Technology Co. Ltd, Hefei 230601, P. R. China;
3. School of Clinical Medical, Anhui Medical University, Hefei 230032, P. R. China;

Corresponding author：

XIE Yao, Email: xieyao@mail.ustc.edu.cn

Keywords：

Canonical correlation analysis; Impedance cardiography; Motion artifacts; Denoising method

DOI：

10.7507/1001-5515.202210059

Video：

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Impedance cardiography (ICG) is essential in evaluating cardiac function in patients with cardiovascular diseases. Aiming at the problem that the measurement of ICG signal is easily disturbed by motion artifacts, this paper introduces a de-noising method based on two-step spectral ensemble empirical mode decomposition (EEMD) and canonical correlation analysis (CCA). Firstly, the first spectral EEMD-CCA was performed between ICG and motion signals, and electrocardiogram (ECG) and motion signals, respectively. The component with the strongest correlation coefficient was set to zero to suppress the main motion artifacts. Secondly, the obtained ECG and ICG signals were subjected to a second spectral EEMD-CCA for further denoising. Lastly, the ICG signal is reconstructed using these share components. The experiment was tested on 30 subjects, and the results showed that the quality of the ICG signal is greatly improved after using the proposed denoising method, which could support the subsequent diagnosis and analysis of cardiovascular diseases.

1.	Hall J E, Hall M E. Guyton and Hall textbook of medical physiology. 14th Edition. Elsevier Health Sciences, 2020.
2.	Zhang Y J, Qu M H, Webster J G, et al. Cardiac output monitoring by impedance cardiography during treadmill exercise. IEEE Transactions on Biomedical Engineering, 1986, 33(11): 1037-1042.
3.	Guyton A C. A continuous cardiac output recorder employing the fick principle. Circulation Research, 1959, 7(4): 661-665.
4.	Louvaris Z, Spetsioti S, Andrianopoulos V, et al. Cardiac output measurement during exercise in COPD: A comparison of dye dilution and impedance cardiography. The Clinical Respiratory Journal, 2019, 13(4): 222-231.
5.	Kay J C, Noble W H. A comparison of thermal and dye dilution methods of determining cardiac output. Can Anaesth Soc J, 1973, 20(3): 347-356.
6.	Moore F A, Haenel J B, Moore E E. Alternatives to Swan-Ganz cardiac output monitoring. Surg Clin North Am, 1991, 71(4): 699-721.
7.	Kubicek W G, Kottke F J, Ramos M U, et al. The Minnesota impedance cardiograph-theory and applications. Bio-Medical Engineering, 1974, 9(9): 410-416.
8.	Choudhari P C, Panse M S. Artifact removal from the radial bioimpedance signal using adaptive wavelet packet transform, Int J Computat Eng Res, 2014, 4(7): 95-101.
9.	Yamamoto Y, Mokushi K. Design and implementation of a digital filter for beat-by-beat impedance cardiography. IEEE Transactions on Biomedical Engineering, 1988, 35(12): 1086-1090.
10.	Mallam M, Rao K C. Efficient reference-free adaptive artifact cancellers for impedance cardiography based remote health care monitoring systems. SpringerPlus, 2016, 5(1): 770.
11.	Hu X, Chen X, Ren R, et al. Adaptive filtering and characteristics extraction for impedance cardiography. Journal of Fiber Bioengineering and Informatics, 2014, 7(1): 81-90.
12.	Stepanov R, Podtaev S, Frick P, et al. Beat-to-beat cardiovascular hemodynamic parameters based on wavelet spectrogram of impedance data. Biomedical Signal Processing and Control, 2017, 36: 50-56.
13.	Ngui W K, Leong M S, Hee L M, et al. Wavelet analysis: mother wavelet selection methods. Applied Mechanics and Materials, 2013, 393: 953-958.
14.	Ishiguro T, Umezu A, Yasuda Y, et al. Modified scaled Fourier linear combiner in thoracic impedance cardiography. Computers in Biology and Medicine, 2006, 36(9): 997-1013.
15.	Ali Sheikh S A, Shah A, Levantsevych O, et al. An open-source automated algorithm for removal of noisy beats for accurate impedance cardiogram analysis. Physiological Measurement, 2020, 41(7): 075002.
16.	Sweeney K T, McLoone S F, Ward T E. The use of ensemble empirical mode decomposition with canonical correlation analysis as a novel artifact removal technique. IEEE Transactions on Biomedical Engineering, 2013, 60(1): 97-105.
17.	Chen X, Xu X, Liu A, et al. The use of multivariate EMD and CCA for denoising muscle artifacts from few-channel EEG recordings. IEEE Transactions on Instrumentation and Measurement, 2018, 67(2): 359-370.
18.	Arunkumar K R, Bhaskar M. Heart rate estimation from wrist-type photoplethysmography signals during physical exercise. Biomedical Signal Processing and Control, 2020, 57: 101790.
19.	Zhang Z, Pi Z, Liu B. TROIKA: a general framework for heart rate monitoring using wrist-type photoplethysmographic signals during intensive physical exercise. IEEE Transactions on Biomedical Engineering, 2015, 62(2): 522-531.
20.	Krafty R T, Hall M. Canonical correlation analysis between time series and static outcomes, with application to the spectral analysis of heart rate variability. Ann Appl Stat, 2013, 7(1): 570-587.
21.	Muñoz J E, Gambús P, Jensen E W, et al. Time-frequency features for impedance cardiography signals during anesthesia using different distribution kernels. Methods of Information in Medicine, 2018, 57(1): e1-e9.
22.	Islam M T, Ahmed S T, Shahnaz C, et al. SPECMAR: fast heart rate estimation from PPG signal using a modified spectral subtraction scheme with composite motion artifacts reference generation. Medical & Biological Engineering & Computing, 2019, 57(3): 689-702.
23.	De Clercq W, Vergult A, Vanrumste B, et al. Canonical correlation analysis applied to remove muscle artifacts from the electroencephalogram. IEEE transactions on Biomedical Engineering, 2006, 53(12): 2583-2587.
24.	Wu Z, Huang N. Ensemble empirical mode decomposition: a noise-assisted data analysis method. Advances in Data Science and Adaptive Analysis, 2009, 1: 1-41.
25.	叶琳琳, 杨丹, 王旭. 基于集合经验分解与改进阈值函数的小波变换心电信号去噪方法研究. 生物医学工程学杂志, 2014, 31(3): 567-571.
26.	Chabchoub S, Mansouri S, Ben Salah R. Signal processing techniques applied to impedance cardiography ICG signals-a review. J Med Eng Technol. 2022, 46(3): 243-260.
27.	张亚丹, 季忠, 谭霞, 等. 用于无创心功能检测的心阻抗微分信号处理. 生物医学工程学杂志, 2019, 36(1): 50-58.
28.	孙阳. 基于胸阻抗法的无创心排量检测系统研制. 深圳: 深圳大学, 2018.
29.	Forouzanfar M, Baker F C, de Zambotti M, et al. Toward a better noninvasive assessment of preejection period: a novel automatic algorithm for B-point detection and correction on thoracic impedance cardiogram. Psychophysiology, 2018, 55(8): e13072.
30.	Bagal U R, Pandey P C, Naidu S M M, et al. Detection of opening and closing of the aortic valve using impedance cardiography and its validation by echocardiography. Biomedical Physics & Engineering Express, 2017, 4(1): 015012.
31.	Ono T, Miyamura M, Yasuda Y, et al. Beat-to-beat evaluation of systolic time intervals during bicycle exercise using impedance cardiography. Tohoku Journal of Experimental Medicine, 2004, 203(1): 17-29.

1. Hall J E, Hall M E. Guyton and Hall textbook of medical physiology. 14th Edition. Elsevier Health Sciences, 2020.
2. Zhang Y J, Qu M H, Webster J G, et al. Cardiac output monitoring by impedance cardiography during treadmill exercise. IEEE Transactions on Biomedical Engineering, 1986, 33(11): 1037-1042.
3. Guyton A C. A continuous cardiac output recorder employing the fick principle. Circulation Research, 1959, 7(4): 661-665.
4. Louvaris Z, Spetsioti S, Andrianopoulos V, et al. Cardiac output measurement during exercise in COPD: A comparison of dye dilution and impedance cardiography. The Clinical Respiratory Journal, 2019, 13(4): 222-231.
5. Kay J C, Noble W H. A comparison of thermal and dye dilution methods of determining cardiac output. Can Anaesth Soc J, 1973, 20(3): 347-356.
6. Moore F A, Haenel J B, Moore E E. Alternatives to Swan-Ganz cardiac output monitoring. Surg Clin North Am, 1991, 71(4): 699-721.
7. Kubicek W G, Kottke F J, Ramos M U, et al. The Minnesota impedance cardiograph-theory and applications. Bio-Medical Engineering, 1974, 9(9): 410-416.
8. Choudhari P C, Panse M S. Artifact removal from the radial bioimpedance signal using adaptive wavelet packet transform, Int J Computat Eng Res, 2014, 4(7): 95-101.
9. Yamamoto Y, Mokushi K. Design and implementation of a digital filter for beat-by-beat impedance cardiography. IEEE Transactions on Biomedical Engineering, 1988, 35(12): 1086-1090.
10. Mallam M, Rao K C. Efficient reference-free adaptive artifact cancellers for impedance cardiography based remote health care monitoring systems. SpringerPlus, 2016, 5(1): 770.
11. Hu X, Chen X, Ren R, et al. Adaptive filtering and characteristics extraction for impedance cardiography. Journal of Fiber Bioengineering and Informatics, 2014, 7(1): 81-90.
12. Stepanov R, Podtaev S, Frick P, et al. Beat-to-beat cardiovascular hemodynamic parameters based on wavelet spectrogram of impedance data. Biomedical Signal Processing and Control, 2017, 36: 50-56.
13. Ngui W K, Leong M S, Hee L M, et al. Wavelet analysis: mother wavelet selection methods. Applied Mechanics and Materials, 2013, 393: 953-958.
14. Ishiguro T, Umezu A, Yasuda Y, et al. Modified scaled Fourier linear combiner in thoracic impedance cardiography. Computers in Biology and Medicine, 2006, 36(9): 997-1013.
15. Ali Sheikh S A, Shah A, Levantsevych O, et al. An open-source automated algorithm for removal of noisy beats for accurate impedance cardiogram analysis. Physiological Measurement, 2020, 41(7): 075002.
16. Sweeney K T, McLoone S F, Ward T E. The use of ensemble empirical mode decomposition with canonical correlation analysis as a novel artifact removal technique. IEEE Transactions on Biomedical Engineering, 2013, 60(1): 97-105.
17. Chen X, Xu X, Liu A, et al. The use of multivariate EMD and CCA for denoising muscle artifacts from few-channel EEG recordings. IEEE Transactions on Instrumentation and Measurement, 2018, 67(2): 359-370.
18. Arunkumar K R, Bhaskar M. Heart rate estimation from wrist-type photoplethysmography signals during physical exercise. Biomedical Signal Processing and Control, 2020, 57: 101790.
19. Zhang Z, Pi Z, Liu B. TROIKA: a general framework for heart rate monitoring using wrist-type photoplethysmographic signals during intensive physical exercise. IEEE Transactions on Biomedical Engineering, 2015, 62(2): 522-531.
20. Krafty R T, Hall M. Canonical correlation analysis between time series and static outcomes, with application to the spectral analysis of heart rate variability. Ann Appl Stat, 2013, 7(1): 570-587.
21. Muñoz J E, Gambús P, Jensen E W, et al. Time-frequency features for impedance cardiography signals during anesthesia using different distribution kernels. Methods of Information in Medicine, 2018, 57(1): e1-e9.
22. Islam M T, Ahmed S T, Shahnaz C, et al. SPECMAR: fast heart rate estimation from PPG signal using a modified spectral subtraction scheme with composite motion artifacts reference generation. Medical & Biological Engineering & Computing, 2019, 57(3): 689-702.
23. De Clercq W, Vergult A, Vanrumste B, et al. Canonical correlation analysis applied to remove muscle artifacts from the electroencephalogram. IEEE transactions on Biomedical Engineering, 2006, 53(12): 2583-2587.
24. Wu Z, Huang N. Ensemble empirical mode decomposition: a noise-assisted data analysis method. Advances in Data Science and Adaptive Analysis, 2009, 1: 1-41.
25. 叶琳琳, 杨丹, 王旭. 基于集合经验分解与改进阈值函数的小波变换心电信号去噪方法研究. 生物医学工程学杂志, 2014, 31(3): 567-571.
26. Chabchoub S, Mansouri S, Ben Salah R. Signal processing techniques applied to impedance cardiography ICG signals-a review. J Med Eng Technol. 2022, 46(3): 243-260.
27. 张亚丹, 季忠, 谭霞, 等. 用于无创心功能检测的心阻抗微分信号处理. 生物医学工程学杂志, 2019, 36(1): 50-58.
28. 孙阳. 基于胸阻抗法的无创心排量检测系统研制. 深圳: 深圳大学, 2018.
29. Forouzanfar M, Baker F C, de Zambotti M, et al. Toward a better noninvasive assessment of preejection period: a novel automatic algorithm for B-point detection and correction on thoracic impedance cardiogram. Psychophysiology, 2018, 55(8): e13072.
30. Bagal U R, Pandey P C, Naidu S M M, et al. Detection of opening and closing of the aortic valve using impedance cardiography and its validation by echocardiography. Biomedical Physics & Engineering Express, 2017, 4(1): 015012.
31. Ono T, Miyamura M, Yasuda Y, et al. Beat-to-beat evaluation of systolic time intervals during bicycle exercise using impedance cardiography. Tohoku Journal of Experimental Medicine, 2004, 203(1): 17-29.

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