Arrhythmia heartbeats classification based on neighborhood preserving embedding algorithm_Journal of Biomedical Engineering

Authors：

GAO Xingjiao ,  LI Zhi , CHEN Shanshan , LI Jian

School of Electronic Information, Sichuan University, Chengdu 610041, P.R.China;

Corresponding author：

LI Zhi, Email: lizhi@scu.edu.cn

Keywords：

arrhythmia; neighborhood preserving embedding; electrocardiogram; support vector machine

DOI：

10.7507/1001-5515.201605045

Video：

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Arrhythmia is a kind of common cardiac electrical activity abnormalities. Heartbeats classification based on electrocardiogram (ECG) is of great significance for clinical diagnosis of arrhythmia. This paper proposes a feature extraction method based on manifold learning, neighborhood preserving embedding (NPE) algorithm, to achieve the automatic classification of arrhythmia heartbeats. With classification system, we obtained low dimensional manifold structure features of high dimensional ECG signals by NPE algorithm, then we inputted the feature vectors into support vector machine (SVM) classifier for heartbeats diagnosis. Based on MIT-BIH arrhythmia database, we clustered 14 classes of arrhythmia heartbeats in the experiment, which yielded a high overall classification accuracy of 98.51%. Experimental result showed that the proposed method was an effective classification method for arrhythmia heartbeats.

Citation： GAOXingjiao, LIZhi, CHENShanshan, LIJian. Arrhythmia heartbeats classification based on neighborhood preserving embedding algorithm. Journal of Biomedical Engineering, 2017, 34(1): 1-6. doi: 10.7507/1001-5515.201605045 Copy

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2.	武扬. 心电特征提取及分类方法研究. 上海: 上海交通大学, 2012.
3.	Zadeh A E, Khazaee A, Ranaee V. Classification of the electrocardiogram signals using supervised classifiers and efficient features. Comput Methods Programs Biomed, 2010, 99(2): 179-194.
4.	Korürek M, Nizam A. Clustering MIT-BIH arrhythmias with Ant Colony Optimization using time domain and PCA compressed wavelet coefficients. Digit Signal Process, 2010, 20(4): 1050-1060.
5.	Lin C C, Yang C M. Heartbeat classification using normalized RR intervals and morphological features. Mathematical Problems in Engineering, 2014, 2014(12): 1-11.
6.	de Chazal P, O'Dwyer M, Reilly R B. Automatic classification of heartbeats using ECG morphology and heartbeat interval features. IEEE Trans Biomed Eng, 2004, 51(7): 1196-1206.
7.	Zhang H, Zhang L Q. ECG analysis based on PCA and Support Vector Machines//International Conference on Neural Networks and Brain. Beijing, China, 2005, 2: 743-747.
8.	Wu Y, Zhang L. ECG classification using ICA features and support vector machines. Neural Information Processing, 2011, 7062: 146-154.
9.	Ye Can, Kumar B V, Coimbra M T. Heartbeat classification using morphological and dynamic features of ECG signals. IEEE Trans Biomed Eng, 2012, 59(10): 2930-2941.
10.	曹林林. 基于流形学习的分类技术. 济南: 山东师范大学, 2013.
11.	Kallas M, Francis C, Kanaan L, et al. Multi-class SVM classification combined with kernel PCA feature extraction of ECG signals//Proceedings of International Conference on Telecommunications. Jounieh, Lebanon, 2012: 1-5.
12.	刘通, 司玉娟, 臧睦君, 等. 基于核主元分析和支持向量机的心拍识别//2015光学精密工程论坛论文集. 长春, 2105: 745-752.
13.	赵勇, 洪文学, 孙士博. 基于多特征和支持向量机的心律失常分类. 生物医学工程学杂志, 2011, 39(2): 292-295.
14.	Li Hongqiang, Liang Huan, Miao Chunjiao, et al. Novel ECG signal classification based on KICA nonlinear feature extraction. Circuits Systems and Signal Processing, 2016, 35(4): 1187-1197.
15.	Tenenbaum J B, de Silva V, Langford J C. A global geometric framework for nonlinear dimensionality reduction. Science, 2000, 290(550): 2319-2323.
16.	Roweis S T, Saul L K. Nonlinear dimensionality reduction by locally linear embedding. Science, 2000, 290(550): 2323-2326.
17.	Belkin M, Niyogi P. Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput, 2003, 15(6): 1373-1396.
18.	Lashgari E, Jahed M, Khalaj B. Manifold learning for ECG arrhythmia recognition//Iranian Conference on Biomedical Engineering. Tehran, Iran, 2013: 126-131.
19.	Vemulapati M. Classification of ECG arrhythmia using manifold learning and support vector machine[R/OL]. (2015-04-02)[2016-5-18]. http://www.usc.edu/CSSF/History/2015/Projects/35186.pdf.
20.	He X, Cai D, Yan S, et al. Neighborhood preserving embedding//Tenth IEEE International Conference on Computer Vision. Beijing, China, 2005: 1208-1213.
21.	Moody G B, Mark R G. The impact of the MIT-BIH arrhythmia database. IEEE Engineering in Medicine and Biology Magazine, 2001, 20(3): 45-50.
22.	Yazdani S, Vesin J M. Adaptive mathematical morphology for QRS fiducial points detection in the ECG//41st Computing in Cardiology Conference. Cambridge, MA, USA, 2014: 725-728.
23.	Yeh Y C, Wang W J. QRS complexes detection for ECG signal: the Difference Operation Method. Comput Methods Programs Biomed, 2008, 91(3): 245-254.
24.	Cortes C, Vapnik V. Support-Vector networks. Mach Learn, 1995, 20(3): 273-297.
25.	胡国胜, 钱玲, 张国红. 支持向量机的多分类算法. 系统工程与电子技术, 2006, 28(1): 127-132.
26.	Chang C C, Lin C J. LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2011, 2(3, SI): 389-396.
27.	Übeyli E D. Adaptive neuro-fuzzy inference system for classification of ECG signals using Lyapunov exponents. Computer Methods & Programs in Biomedicine, 2009, 93(3): 313-321.
28.	Karpagachelvi D S. Classification of ECG signals using particle swarm optimization and extreme learning machine. International Journal of Engineering Sciences & Research Technology, 2014, 3(7): 95-102.
29.	Chen Y H, Yu S N. Selection of effective features for ECG beat recognition based on nonlinear correlations. Artif Intell Med, 2012, 54(1): 43-52.

1. 陆海燕, 陈翠杰, 娄丽艳, 等. 心律失常患者的临床观察与护理干预. 中国实用医药, 2013, 8(2): 209-210.
2. 武扬. 心电特征提取及分类方法研究. 上海: 上海交通大学, 2012.
3. Zadeh A E, Khazaee A, Ranaee V. Classification of the electrocardiogram signals using supervised classifiers and efficient features. Comput Methods Programs Biomed, 2010, 99(2): 179-194.
4. Korürek M, Nizam A. Clustering MIT-BIH arrhythmias with Ant Colony Optimization using time domain and PCA compressed wavelet coefficients. Digit Signal Process, 2010, 20(4): 1050-1060.
5. Lin C C, Yang C M. Heartbeat classification using normalized RR intervals and morphological features. Mathematical Problems in Engineering, 2014, 2014(12): 1-11.
6. de Chazal P, O'Dwyer M, Reilly R B. Automatic classification of heartbeats using ECG morphology and heartbeat interval features. IEEE Trans Biomed Eng, 2004, 51(7): 1196-1206.
7. Zhang H, Zhang L Q. ECG analysis based on PCA and Support Vector Machines//International Conference on Neural Networks and Brain. Beijing, China, 2005, 2: 743-747.
8. Wu Y, Zhang L. ECG classification using ICA features and support vector machines. Neural Information Processing, 2011, 7062: 146-154.
9. Ye Can, Kumar B V, Coimbra M T. Heartbeat classification using morphological and dynamic features of ECG signals. IEEE Trans Biomed Eng, 2012, 59(10): 2930-2941.
10. 曹林林. 基于流形学习的分类技术. 济南: 山东师范大学, 2013.
11. Kallas M, Francis C, Kanaan L, et al. Multi-class SVM classification combined with kernel PCA feature extraction of ECG signals//Proceedings of International Conference on Telecommunications. Jounieh, Lebanon, 2012: 1-5.
12. 刘通, 司玉娟, 臧睦君, 等. 基于核主元分析和支持向量机的心拍识别//2015光学精密工程论坛论文集. 长春, 2105: 745-752.
13. 赵勇, 洪文学, 孙士博. 基于多特征和支持向量机的心律失常分类. 生物医学工程学杂志, 2011, 39(2): 292-295.
14. Li Hongqiang, Liang Huan, Miao Chunjiao, et al. Novel ECG signal classification based on KICA nonlinear feature extraction. Circuits Systems and Signal Processing, 2016, 35(4): 1187-1197.
15. Tenenbaum J B, de Silva V, Langford J C. A global geometric framework for nonlinear dimensionality reduction. Science, 2000, 290(550): 2319-2323.
16. Roweis S T, Saul L K. Nonlinear dimensionality reduction by locally linear embedding. Science, 2000, 290(550): 2323-2326.
17. Belkin M, Niyogi P. Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput, 2003, 15(6): 1373-1396.
18. Lashgari E, Jahed M, Khalaj B. Manifold learning for ECG arrhythmia recognition//Iranian Conference on Biomedical Engineering. Tehran, Iran, 2013: 126-131.
19. Vemulapati M. Classification of ECG arrhythmia using manifold learning and support vector machine[R/OL]. (2015-04-02)[2016-5-18]. http://www.usc.edu/CSSF/History/2015/Projects/35186.pdf.
20. He X, Cai D, Yan S, et al. Neighborhood preserving embedding//Tenth IEEE International Conference on Computer Vision. Beijing, China, 2005: 1208-1213.
21. Moody G B, Mark R G. The impact of the MIT-BIH arrhythmia database. IEEE Engineering in Medicine and Biology Magazine, 2001, 20(3): 45-50.
22. Yazdani S, Vesin J M. Adaptive mathematical morphology for QRS fiducial points detection in the ECG//41st Computing in Cardiology Conference. Cambridge, MA, USA, 2014: 725-728.
23. Yeh Y C, Wang W J. QRS complexes detection for ECG signal: the Difference Operation Method. Comput Methods Programs Biomed, 2008, 91(3): 245-254.
24. Cortes C, Vapnik V. Support-Vector networks. Mach Learn, 1995, 20(3): 273-297.
25. 胡国胜, 钱玲, 张国红. 支持向量机的多分类算法. 系统工程与电子技术, 2006, 28(1): 127-132.
26. Chang C C, Lin C J. LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2011, 2(3, SI): 389-396.
27. Übeyli E D. Adaptive neuro-fuzzy inference system for classification of ECG signals using Lyapunov exponents. Computer Methods & Programs in Biomedicine, 2009, 93(3): 313-321.
28. Karpagachelvi D S. Classification of ECG signals using particle swarm optimization and extreme learning machine. International Journal of Engineering Sciences & Research Technology, 2014, 3(7): 95-102.
29. Chen Y H, Yu S N. Selection of effective features for ECG beat recognition based on nonlinear correlations. Artif Intell Med, 2012, 54(1): 43-52.

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