Research on Clinical Electrocardiogram Classification Algorithm Based on Ensemble Learning_Journal of Biomedical Engineering

Authors：

JINLinpeng ^1,2 ,  DONGJun ¹

1. Suzhou Institute of Nano-tech and Nano-bionics, Chinese Academy of Sciences, Suzhou 215123, China;
2. University of Chinese Academy of Sciences, Beijing 100049, China;

Corresponding?author：

DONGJun, Email: jdong2010@sinano.ac.cn

Keywords：

electrocardiogram; ensemble learning; deep learning; Convolutional Neural Networks; classification

DOI：

10.7507/1001-5515.20160134

Video：

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With the increasing number of electrocardiogram (ECG) data, extensive application requirements of computer-aided ECG analysis have occurred. In the paper, we propose a variety of strategies to improve the performance of clinical ECG classification algorithm based on Lead Convolutional Neural Network (LCNN). Firstly, we obtained two classifiers by using different preprocessing methods and training methods in the study. Then, we applied the multiple output prediction method to both of them independently. Finally, the Bayesian approach was employed to fuse them. Tests conducted using more than 150 000 ECG records showed that the proposed method had an accuracy of 85.04% and the area under receiver operating characteristic curve (AUC) was 0.918 5, which significantly outperforms traditional methods based on feature extraction techniques.

Citation： JIN Linpeng, DONG Jun. Research on Clinical Electrocardiogram Classification Algorithm Based on Ensemble Learning. Journal of Biomedical Engineering, 2016, 33(5): 825-833. doi: 10.7507/1001-5515.20160134 Copy

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2.	王麗蘋, 董軍.心電圖模式分類方法研究進展與分析[J].中國生物醫學工程學報, 2010, 29(6):916-925.
3.	CLIFFORD G D, AZUAJE F, MCSHARRY P E. Advanced methods and tools for ECG data analysis[M]. London:Artech House, 2006.
4.	ZHANG Zhancheng, DONG Jun, LUO Xiaoqing, et al. Heartbeat classification using disease-specific feature selection[J]. Comput Biol Med, 2014, 46:79-89.
5.	MAR T, ZAUNSEDER S, MART?NEZ J P, et al. Optimization of ECG classification by means of feature selection[J]. IEEE Trans Biomed Eng, 2011, 58(8):2168-2177.
6.	MINHAS F U, ARIF M. Robust electrocardiogram (ECG) beat classification using discrete wavelet transform[J]. Physiol Meas, 2008, 29(5):555-570.
7.	MARTIS R J, CHAKRABORTY C, RAY A K. A two-stage mechanism for registration and classification of ECG using Gaussian mixture model[J]. Pattern Recognit, 2009, 42(11):2979-2988.
8.	KUNDU M, NASIPURI M, BASU D K. Knowledge-based ECG interpretation:a critical review[J]. Pattern Recognit, 2000, 33(3):351-373.
9.	TSIPOURAS M G, FOTIADIS D I, SIDERIS D. An arrhythmia classification system based on the RR-interval signal[J]. Artif Intell Med, 2005, 33(3):237-250.
10.	HUANG Huifang, LIU Jie, ZHU Qiang, et al. Detection of inter-patient left and right bundle branch block heartbeats in ECG using ensemble classifiers[J]. Biomed Eng Online, 2014, 13:1-22.
11.	MARTIS R J, ACHARYA U R, ADELI H A, et al. Computer aided diagnosis of atrial arrhythmia using dimensionality reduction methods on transform domain representation[J]. Biomed Signal Process Control, 2014, 13:295-305.
12.	HUANG Huifang, LIU Jie, ZHU Qiang, et al. A new hierarchical method for inter-patient heartbeat classification using random projections and RR intervals[J]. Biomed Eng Online, 2014, 13:1-26.
13.	DAQROUQ K, ALKHATEEB A, AJOUR M N, et al. Neural network and wavelet average framing percentage energy for atrial fibrillation classification[J]. Comput Methods Programs Biomed, 2014, 113(3):919-926.
14.	DE CHAZAL P, O'DWYER M, REILLY R B. Automatic classification of heartbeats using ECG morphology and heartbeat interval features[J]. IEEE Trans Biomed Eng, 2004, 51(7):1196-1206.
15.	YE Can, KUMAR B V, COIMBRA M T. Heartbeat classification using morphological and dynamic features of ECG signals[J]. IEEE Trans Biomed Eng, 2012, 59(10):2930-2941.
16.	SINGH Y N. Individual identification using linear projection of heartbeat features[J]. Applied Computational Intelligence and Soft Computing, 2014:1-14.
17.	ANSI/AAMI EC57/Ed.3. Testing and reporting performance results of cardiac rhythm and ST segment measurement algorithms[S]. ANSI/AAMI, 1998, Rev 2012.
18.	GOLDBERGER A L, AMARAL L A, GLASS L, et al. PhysioBank, PhysioToolkit, and PhysioNet:components of a new research resource for complex physiologic signals[J]. Circulation, 2000, 101(23):E215-E220.
19.	DONG Jun, ZHANG Jiawei, ZHU Honghai, et al. Wearable ECG monitors and its remote diagnosis service platform[J]. IEEE Intell Syst, 2012, 27(6):36-43.
20.	ZHANG Jiawei, LIU Xia, DONG Jun. CCDD:an enhanced Standard ECG database with its management and annotation tools[J]. Int J Artif Intell Tools, 2012, 21(5):1-26.
21.	朱洪海.心電圖自動識別的關鍵算法設計及多體征遠程監護系統研制[D].北京:中國科學院大學, 2013.
22.	王麗蘋.融合領域知識的心電圖分類方法研究[D].上海:華東師范大學, 2013.
23.	金林鵬, 董軍.面向臨床心電圖分析的深層學習算法研究[J].中國科學:信息科學, 2015, 45(3):398-416.
24.	THAKOR N V, WEBSTER J G, TOMPKINS W J. Estimation of QRS complex power spectra for design of a QRS filter[J]. IEEE transactions on bio-medical engineering, 1984, 31(11):702-706.
25.	國家食品藥品監督管理局.YY 1139-2000單道和多道心電圖機[S].北京:國家食品藥品監督管理局, 2000.
26.	LECUN Y, BOTTOU L, BENGIO Y, et al. Gradient-based learning applied to document recognition[C]//Proceedings of the IEEE. New York:IEEE Press, 1998:2278-2324.
27.	RUMELHART D, HINTON G, WILLIAMS R. Parallel distributed processing[M]. Cambridge:MIT Press, 1986:318-362.
28.	LI M, ZHANG T, CHEN Y Q, et al. Efficient mini-batch training for stochastic optimization[C]//Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA, 2014:661-670.
29.	VOGL T P, MANGIS J K, RIGLER A K, et al. Accelerating the convergence of the back-propagation method[J]. Biol Cybern, 1988, 59(4/5):257-263.
30.	KITTLER J, HATEF M, DUIN R, et al. On combining classifiers[J]. IEEE Trans Pattern Anal Mach Intell, 1998, 20(3):226-239.
31.	BATUWITA R, PALADE V. A new performance measure for class imbalance learning. Application to bioinformatics problem[C]//International Conference on Machine Learning and Applications. Miami Beach, 2009:545-550.
32.	LIU X. Atlas of classical electrocardiograms[M]. Shanghai:Shanghai Science and Technology Press, 2011.
33.	ZHU Honghai, DONG Jun. An R-peak detection method based on peaks of Shannon energy envelope[J]. Biomed Signal Process Control, 2013, 8(5):466-474.
34.	LAKE D E, MOORMAN J R. Accurate estimation of entropy in very short physiological time series:the problem of atrial fibrillation detection in implanted ventricular devices[J]. Am J Physiol Heart Circ Physiol, 2011, 300(1):H319-H325.
35.	HAKACOVA N, TR?G?RDH-JOHANSSON E, WAGNER G S, et al. Computer-based rhythm diagnosis and its possible influence on nonexpert electrocardiogram readers[J]. J Electrocardiol, 2012, 45(1):18-22.

1. 董軍, 徐淼, 詹聰明, 等.心電圖識別與分類:方法、問題和新途徑[J].生物醫學工程學雜志, 2007, 24(6):1224-1229.
2. 王麗蘋, 董軍.心電圖模式分類方法研究進展與分析[J].中國生物醫學工程學報, 2010, 29(6):916-925.
3. CLIFFORD G D, AZUAJE F, MCSHARRY P E. Advanced methods and tools for ECG data analysis[M]. London:Artech House, 2006.
4. ZHANG Zhancheng, DONG Jun, LUO Xiaoqing, et al. Heartbeat classification using disease-specific feature selection[J]. Comput Biol Med, 2014, 46:79-89.
5. MAR T, ZAUNSEDER S, MART?NEZ J P, et al. Optimization of ECG classification by means of feature selection[J]. IEEE Trans Biomed Eng, 2011, 58(8):2168-2177.
6. MINHAS F U, ARIF M. Robust electrocardiogram (ECG) beat classification using discrete wavelet transform[J]. Physiol Meas, 2008, 29(5):555-570.
7. MARTIS R J, CHAKRABORTY C, RAY A K. A two-stage mechanism for registration and classification of ECG using Gaussian mixture model[J]. Pattern Recognit, 2009, 42(11):2979-2988.
8. KUNDU M, NASIPURI M, BASU D K. Knowledge-based ECG interpretation:a critical review[J]. Pattern Recognit, 2000, 33(3):351-373.
9. TSIPOURAS M G, FOTIADIS D I, SIDERIS D. An arrhythmia classification system based on the RR-interval signal[J]. Artif Intell Med, 2005, 33(3):237-250.
10. HUANG Huifang, LIU Jie, ZHU Qiang, et al. Detection of inter-patient left and right bundle branch block heartbeats in ECG using ensemble classifiers[J]. Biomed Eng Online, 2014, 13:1-22.
11. MARTIS R J, ACHARYA U R, ADELI H A, et al. Computer aided diagnosis of atrial arrhythmia using dimensionality reduction methods on transform domain representation[J]. Biomed Signal Process Control, 2014, 13:295-305.
12. HUANG Huifang, LIU Jie, ZHU Qiang, et al. A new hierarchical method for inter-patient heartbeat classification using random projections and RR intervals[J]. Biomed Eng Online, 2014, 13:1-26.
13. DAQROUQ K, ALKHATEEB A, AJOUR M N, et al. Neural network and wavelet average framing percentage energy for atrial fibrillation classification[J]. Comput Methods Programs Biomed, 2014, 113(3):919-926.
14. DE CHAZAL P, O'DWYER M, REILLY R B. Automatic classification of heartbeats using ECG morphology and heartbeat interval features[J]. IEEE Trans Biomed Eng, 2004, 51(7):1196-1206.
15. YE Can, KUMAR B V, COIMBRA M T. Heartbeat classification using morphological and dynamic features of ECG signals[J]. IEEE Trans Biomed Eng, 2012, 59(10):2930-2941.
16. SINGH Y N. Individual identification using linear projection of heartbeat features[J]. Applied Computational Intelligence and Soft Computing, 2014:1-14.
17. ANSI/AAMI EC57/Ed.3. Testing and reporting performance results of cardiac rhythm and ST segment measurement algorithms[S]. ANSI/AAMI, 1998, Rev 2012.
18. GOLDBERGER A L, AMARAL L A, GLASS L, et al. PhysioBank, PhysioToolkit, and PhysioNet:components of a new research resource for complex physiologic signals[J]. Circulation, 2000, 101(23):E215-E220.
19. DONG Jun, ZHANG Jiawei, ZHU Honghai, et al. Wearable ECG monitors and its remote diagnosis service platform[J]. IEEE Intell Syst, 2012, 27(6):36-43.
20. ZHANG Jiawei, LIU Xia, DONG Jun. CCDD:an enhanced Standard ECG database with its management and annotation tools[J]. Int J Artif Intell Tools, 2012, 21(5):1-26.
21. 朱洪海.心電圖自動識別的關鍵算法設計及多體征遠程監護系統研制[D].北京:中國科學院大學, 2013.
22. 王麗蘋.融合領域知識的心電圖分類方法研究[D].上海:華東師范大學, 2013.
23. 金林鵬, 董軍.面向臨床心電圖分析的深層學習算法研究[J].中國科學:信息科學, 2015, 45(3):398-416.
24. THAKOR N V, WEBSTER J G, TOMPKINS W J. Estimation of QRS complex power spectra for design of a QRS filter[J]. IEEE transactions on bio-medical engineering, 1984, 31(11):702-706.
25. 國家食品藥品監督管理局.YY 1139-2000單道和多道心電圖機[S].北京:國家食品藥品監督管理局, 2000.
26. LECUN Y, BOTTOU L, BENGIO Y, et al. Gradient-based learning applied to document recognition[C]//Proceedings of the IEEE. New York:IEEE Press, 1998:2278-2324.
27. RUMELHART D, HINTON G, WILLIAMS R. Parallel distributed processing[M]. Cambridge:MIT Press, 1986:318-362.
28. LI M, ZHANG T, CHEN Y Q, et al. Efficient mini-batch training for stochastic optimization[C]//Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA, 2014:661-670.
29. VOGL T P, MANGIS J K, RIGLER A K, et al. Accelerating the convergence of the back-propagation method[J]. Biol Cybern, 1988, 59(4/5):257-263.
30. KITTLER J, HATEF M, DUIN R, et al. On combining classifiers[J]. IEEE Trans Pattern Anal Mach Intell, 1998, 20(3):226-239.
31. BATUWITA R, PALADE V. A new performance measure for class imbalance learning. Application to bioinformatics problem[C]//International Conference on Machine Learning and Applications. Miami Beach, 2009:545-550.
32. LIU X. Atlas of classical electrocardiograms[M]. Shanghai:Shanghai Science and Technology Press, 2011.
33. ZHU Honghai, DONG Jun. An R-peak detection method based on peaks of Shannon energy envelope[J]. Biomed Signal Process Control, 2013, 8(5):466-474.
34. LAKE D E, MOORMAN J R. Accurate estimation of entropy in very short physiological time series:the problem of atrial fibrillation detection in implanted ventricular devices[J]. Am J Physiol Heart Circ Physiol, 2011, 300(1):H319-H325.
35. HAKACOVA N, TR?G?RDH-JOHANSSON E, WAGNER G S, et al. Computer-based rhythm diagnosis and its possible influence on nonexpert electrocardiogram readers[J]. J Electrocardiol, 2012, 45(1):18-22.

Journal of Biomedical Engineering

Research on Clinical Electrocardiogram Classification Algorithm Based on Ensemble Learning

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