Research on malignant arrhythmia detection algorithm using neural network optimized by genetic algorithm_Journal of Biomedical Engineering

Authors：

YU Ming , CHEN Feng , ZHANG Guang , LI Liangzhe , WANG Chunchen , ZHAN Ningbo , GU Biao , WEI Jing ,  WU Taihu

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

Corresponding author：

WU Taihu, Email: wutaihu@vip.sina.com

Keywords：

cardiac arrest; automated external defibrillators; genetic algorithm; back propagation neural network

DOI：

10.7507/1001-5515.201612066

Video：

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Abstract Full text Figures/Tables Video References Cited by

Detection and classification of malignant arrhythmia are key tasks of automated external defibrillators. In this paper, 21 metrics extracted from existing algorithms were studied by retrospective analysis. Based on these metrics, a back propagation neural network optimized by genetic algorithm was constructed. A total of 1,343 electrocardiogram samples were included in the analysis. The results of the experiments indicated that this network had a good performance in classification of sinus rhythm, ventricular fibrillation, ventricular tachycardia and asystole. The balanced accuracy on test dataset reached up to 99.06%. It illustrates that our proposed detection algorithm is obviously superior to existing algorithms. The application of the algorithm in the automated external defibrillators will further improve the reliability of rhythm analysis before defibrillation and ultimately improve the survival rate of cardiac arrest.

Citation： YU Ming, CHEN Feng, ZHANG Guang, LI Liangzhe, WANG Chunchen, ZHAN Ningbo, GU Biao, WEI Jing, WU Taihu. Research on malignant arrhythmia detection algorithm using neural network optimized by genetic algorithm. Journal of Biomedical Engineering, 2017, 34(3): 421-430. doi: 10.7507/1001-5515.201612066 Copy

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9.	Goldberger A L, Amaral L A, Glass L, et al. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation, 2000, 101(23): E215-E220.
10.	Li Q, Mark R G, Clifford G D. Robust heart rate estimation from multiple asynchronous noisy sources using signal quality indices and a Kalman filter. Physiol Meas, 2008, 29(1): 15-32.
11.	Kuo S, Dillman R. Computer detection of ventricular fibrillation. Comput Cardiol, 1978: 347-349.
12.	Barro S, Ruiz R, Cabello D, et al. Algorithmic sequential decision-making in the frequency domain for Life threatening ventricular arrhythmias and imitative artefacts: a diagnostic system. J Biomed Eng, 1989, 11(4): 320-328.
13.	Jekova I. Shock advisory tool: Detection of life-threatening cardiac arrhythmias and shock success prediction by means of a common parameter set. Biomed Signal Process Control, 2007, 2(1): 25-33.
14.	Zhang X S, Zhu Y S, Thakor N V, et al. Detecting ventricular tachycardia and fibrillation by complexity measure. IEEE Trans Biomed Eng, 1999, 46(5): 548-555.
15.	Thakor N V, Zhu Y S, Pan K Y. Ventricular tachycardia and fibrillation detection by a sequential hypothesis testing algorithm. IEEE Trans Biomed Eng, 1990, 37(9): 837-843.
16.	Amann A, Tratnig R, Unterkofler K. Detecting ventricular fibrillation by time-delay methods. IEEE Trans Biomed Eng, 2007, 54(1): 174-177.
17.	Torres M E, Gamero L G, D’attellis C E. Detection of changes in nonlinear dynamical systems using multiresolution entropy. INRIA, 1996.
18.	Li Qiao, Rajagopalan C, Clifford G D. Ventricular fibrillation and tachycardia classification using a machine learning approach. IEEE Trans Biomed Eng, 2014, 61(6): 1607-1613.
19.	Alonso-Atienza F, Morgado E, Fernández-Martínez L, et al. Detection of life-threatening arrhythmias using feature selection and support vector machines. IEEE Trans Biomed Eng, 2014, 61(3): 832-840.
20.	Figuera C, Irusta U, Morgado E, et al. Machine learning techniques for the detection of shockable rhythms in automated external defibrillators. PLoS One, 2016, 11(7): e0159654.

1. Mozaffarian D, Benjamin E J, Go A S, et al. Heart disease and stroke statistics-2016 update a report from the American heart association. Circulation, 2016, 133(4): e38-e360.
2. Link M S, Berkow L C, Kudenchuk P J, et al. Part 7: adult advanced cardiovascular Life support. Circulation, 2015, 132(18 suppl 2): 444-464.
3. Jekova I, Krasteva V. Real time detection of ventricular fibrillation and tachycardia. Physiol Meas, 2004, 25(5): 1167-1178.
4. Clayton R H, Murray A, Campbell R W. Recognition of ventricular fibrillation using neural networks. Med Biol Eng Comput, 1994, 32(2): 217-220.
5. Chen S, Thakor N V, Mower M M. Ventricular fibrillation detection by a regression test on the autocorrelation function. Med Biol Eng Comput, 1987, 25(3): 241-249.
6. Amann A, Tratnig R, Unterkofler K. Reliability of old and new ventricular fibrillation detection algorithms for automated external defibrillators. Biomed Eng Online, 2005, 4(1): 60.
7. Clayton R H, Murray A, Campbell R W. Comparison of four techniques for recognition of ventricular fibrillation from the surface ECG. Med Biol Eng Comput, 1993, 31(2): 111-117.
8. Jekova I. Comparison of five algorithms for the detection of ventricular fibrillation from the surface ECG. Physiol Meas, 2000, 21(4): 429-439.
9. Goldberger A L, Amaral L A, Glass L, et al. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation, 2000, 101(23): E215-E220.
10. Li Q, Mark R G, Clifford G D. Robust heart rate estimation from multiple asynchronous noisy sources using signal quality indices and a Kalman filter. Physiol Meas, 2008, 29(1): 15-32.
11. Kuo S, Dillman R. Computer detection of ventricular fibrillation. Comput Cardiol, 1978: 347-349.
12. Barro S, Ruiz R, Cabello D, et al. Algorithmic sequential decision-making in the frequency domain for Life threatening ventricular arrhythmias and imitative artefacts: a diagnostic system. J Biomed Eng, 1989, 11(4): 320-328.
13. Jekova I. Shock advisory tool: Detection of life-threatening cardiac arrhythmias and shock success prediction by means of a common parameter set. Biomed Signal Process Control, 2007, 2(1): 25-33.
14. Zhang X S, Zhu Y S, Thakor N V, et al. Detecting ventricular tachycardia and fibrillation by complexity measure. IEEE Trans Biomed Eng, 1999, 46(5): 548-555.
15. Thakor N V, Zhu Y S, Pan K Y. Ventricular tachycardia and fibrillation detection by a sequential hypothesis testing algorithm. IEEE Trans Biomed Eng, 1990, 37(9): 837-843.
16. Amann A, Tratnig R, Unterkofler K. Detecting ventricular fibrillation by time-delay methods. IEEE Trans Biomed Eng, 2007, 54(1): 174-177.
17. Torres M E, Gamero L G, D’attellis C E. Detection of changes in nonlinear dynamical systems using multiresolution entropy. INRIA, 1996.
18. Li Qiao, Rajagopalan C, Clifford G D. Ventricular fibrillation and tachycardia classification using a machine learning approach. IEEE Trans Biomed Eng, 2014, 61(6): 1607-1613.
19. Alonso-Atienza F, Morgado E, Fernández-Martínez L, et al. Detection of life-threatening arrhythmias using feature selection and support vector machines. IEEE Trans Biomed Eng, 2014, 61(3): 832-840.
20. Figuera C, Irusta U, Morgado E, et al. Machine learning techniques for the detection of shockable rhythms in automated external defibrillators. PLoS One, 2016, 11(7): e0159654.

Journal of Biomedical Engineering

Research on malignant arrhythmia detection algorithm using neural network optimized by genetic algorithm

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