Adaptive Cardiopulmonary Resuscitation Artifacts Elimination Algorithm Based on Empirical Mode Decomposition and Independent Component Analysis_Journal of Biomedical Engineering

Authors：

YUMing , CHENFeng , ZHANGGuang , LILiangzhe , WANGChunchen , WANGDan , ZHANNingbo , GUBiao ,  WUTaihu

Institute of Medical Equipment, Academy of Military Medical Science, Tianjin 300161, China;

Corresponding author：

WUTaihu, Email: wutaihu@vip.sina.com

Keywords：

cardiopulmonary resuscitation; empirical mode decomposition; independent component analysis; back propagation neural network

DOI：

10.7507/1001-5515.201600135

Video：

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Abstract Full text Figures/Tables Video References Cited by

Artifacts produced by chest compression during cardiopulmonary resuscitation (CPR) seriously affect the reliability of shockable rhythm detection algorithms. In this paper, we proposed an adaptive CPR artifacts elimination algorithm without needing any reference channels. The clean electrocardiogram (ECG) signals can be extracted from the corrupted ECG signals by incorporating empirical mode decomposition (EMD) and independent component analysis (ICA). For evaluating the performance of the proposed algorithm, a back propagation neural network was constructed to implement the shockable rhythm detection. A total of 1 484 corrupted ECG samples collected from pigs were included in the analysis. The results of the experiments indicated that this method would greatly reduce the effects of the CPR artifacts and thereby increase the accuracy of the shockable rhythm detection algorithm.

Citation： YU Ming, CHEN Feng, ZHANG Guang, LI Liangzhe, WANG Chunchen, WANG Dan, ZHAN Ningbo, GU Biao, WU Taihu. Adaptive Cardiopulmonary Resuscitation Artifacts Elimination Algorithm Based on Empirical Mode Decomposition and Independent Component Analysis. Journal of Biomedical Engineering, 2016, 33(5): 834-841. doi: 10.7507/1001-5515.201600135 Copy

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18.	LIN L Y, LO M T, CHIANG W C, et al. A new way to analyze resuscitation quality by reviewing automatic external defibrillator data[J]. Resuscitation, 2012, 83(2):171-176.
19.	REHMAN N U, MANDIC D P. Filter bank property of multivariate empirical mode decomposition[J]. IEEE Transactions on Signal Process, 2011, 59(5):2421-2426.
20.	HYVÄRINEN A. Fast and robust fixed-point algorithms for independent component analysis[J]. IEEE Trans Neural Netw, 1999, 10(3):626-634.
21.	HYVÄRINEN A, OJA E. A fast fixed-point algorithm for independent component analysis[J]. Neural Computation, 1997, 9(7):1483-1492.
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23.	CLAYTON R H, MURRAY A, CAMPBELL R W. Recognition of ventricular fibrillation using neural networks[J]. Med Biol Eng Comput, 1994, 32(2):217-220.
24.	JAVADI M. Combining neural networks and ANFIS classifiers for supervised examining of electrocardiogram beats[J]. J Med Eng Technol, 2013, 37(8):484-497.
25.	LI Qiao, RAJAGOPALAN C, CLIFFORD G D. Ventricular fibrillation and tachycardia classification using a machine learning approach[J]. IEEE Trans Biomed Eng, 2014, 61(6):1607-1613.
26.	JEKOVA I, KRASTEVA V. Real time detection of ventricular fibrillation and tachycardia[J]. Physiol Meas, 2004, 25(5):1167-1178.
27.	KUO S, DILLMAN R. Computer detection of ventricular fibrillation[J]. Comput Cardiol, 1978:347-349.

1. ZHENG Z J, CROFT J B, GILES W H, et al. Sudden cardiac death in the United States, 1989 to 1998[J]. Circulation, 2001, 104(18):2158-2163.
2. REA T D, EISENBERG M S, SINIBALDI G, et al. Incidence of EMS-treated out-of-hospital cardiac arrest in the United States[J]. Resuscitation, 2004, 63(1):17-24.
3. WAALEWIJN R A, DE VOS R, KOSTER R W. Out-of-hospital cardiac arrests in Amsterdam and its surrounding areas:results from the Amsterdam resuscitation study (ARREST) in 'Utstein' style[J]. Resuscitation, 1998, 38(3):157-167.
4. DEAKIN C D, NOLAN J P, SUNDE K, et al. European resuscitation council guidelines for resuscitation 2010 section 3. Electrical therapies:automated external defibrillators, defibrillation, cardioversion and pacing[J]. Resuscitation, 2010, 81(10):1293-1304.
5. AMANN A, TRATNIG R, UNTERKOFLER K. Reliability of old and new ventricular fibrillation detection algorithms for automated external defibrillators[J]. Biomed Eng Online, 2005, 4:60.
6. SATO Y, WEIL M H, SUN S, et al. Adverse effects of interrupting precordial compression during cardiopulmonary resuscitation[J]. Crit Care Med, 1997, 25(5):733-736.
7. VALENZUELA T D, KERN K B, CLARK L L, et al. Interruptions of chest compressions during emergency medical systems resuscitation[J]. Circulation, 2005, 112(9):1259-1265.
8. STROHMENGER H U, LINDNER K H, KELLER A, et al. Spectral analysis of ventricular fibrillation and closed-chest cardiopulmonary resuscitation[J]. Resuscitation, 1996, 33(2):155-161.
9. LANGHELLE A, EFTESTØL T, MYKLEBUST H, et al. Reducing CPR artefacts in ventricular fibrillation in vitro[J]. Resuscitation, 2001, 48(3):279-291.
10. EILEVSTJØNN J, EFTESTØL T, AASE S O, et al. Feasibility of shock advice analysis during CPR through removal of CPR artefacts from the human ECG[J]. Resuscitation, 2004, 61(2):131-141.
11. IRUSTA U, RUIZ J, DE GAUNA S R, et al. A least mean-square filter for the estimation of the cardiopulmonary resuscitation artifact based on the frequency of the compressions[J]. IEEE Trans Biomed Eng, 2009, 56(4):1052-1062.
12. GONG Yushun, YU Tao, CHEN Bihua, et al. Removal of cardiopulmonary resuscitation artifacts with an enhanced adaptive filtering method:an experimental trial[J]. Biomed Res Int, 2014, 2014:140438.
13. LO M T, LIN L Y, HSIEH W H, et al. A new method to estimate the amplitude spectrum analysis of ventricular fibrillation during cardiopulmonary resuscitation[J]. Resuscitation, 2013, 84(11):1505-1511.
14. RUIZ DE GAUNA S, RUIZ J, IRUSTA U, et al. A method to remove CPR artefacts from human ECG using only the recorded ECG[J]. Resuscitation, 2008, 76(2):271-278.
15. THAKOR N V, MOREAU D. Design and analysis of quantised coefficient digital filters:application to biomedical signal processing with microprocessors[J]. Med Biol Eng Comput, 1987, 25(1):18-25.
16. THAKOR N V, ZHU Y S, PAN K Y. Ventricular tachycardia and fibrillation detection by a sequential hypothesis testing algorithm[J]. IEEE Trans Biomed Eng, 1990, 37(9):837-843.
17. HUANG Ne, SHEN Z, LONG S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proceedings of the Royal Society A-Mathematical Physical and Engineering Sciences, 1998, 454(1971):903-995.
18. LIN L Y, LO M T, CHIANG W C, et al. A new way to analyze resuscitation quality by reviewing automatic external defibrillator data[J]. Resuscitation, 2012, 83(2):171-176.
19. REHMAN N U, MANDIC D P. Filter bank property of multivariate empirical mode decomposition[J]. IEEE Transactions on Signal Process, 2011, 59(5):2421-2426.
20. HYVÄRINEN A. Fast and robust fixed-point algorithms for independent component analysis[J]. IEEE Trans Neural Netw, 1999, 10(3):626-634.
21. HYVÄRINEN A, OJA E. A fast fixed-point algorithm for independent component analysis[J]. Neural Computation, 1997, 9(7):1483-1492.
22. HYVÄRINEN A, OJA E. Independent component analysis:algorithms and applications[J]. Neural Netw, 2000, 13(4/5):411-430.
23. CLAYTON R H, MURRAY A, CAMPBELL R W. Recognition of ventricular fibrillation using neural networks[J]. Med Biol Eng Comput, 1994, 32(2):217-220.
24. JAVADI M. Combining neural networks and ANFIS classifiers for supervised examining of electrocardiogram beats[J]. J Med Eng Technol, 2013, 37(8):484-497.
25. LI Qiao, RAJAGOPALAN C, CLIFFORD G D. Ventricular fibrillation and tachycardia classification using a machine learning approach[J]. IEEE Trans Biomed Eng, 2014, 61(6):1607-1613.
26. JEKOVA I, KRASTEVA V. Real time detection of ventricular fibrillation and tachycardia[J]. Physiol Meas, 2004, 25(5):1167-1178.
27. KUO S, DILLMAN R. Computer detection of ventricular fibrillation[J]. Comput Cardiol, 1978:347-349.

Journal of Biomedical Engineering

Adaptive Cardiopulmonary Resuscitation Artifacts Elimination Algorithm Based on Empirical Mode Decomposition and Independent Component Analysis

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