Heart Beat Classification Using Particle Swarm Optimization

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Ali Khazaee 1,*

1. Department of Electrical Engineering, Bojnourd Branch, Islamic Azad University, Bojnourd, Iran

* Corresponding author.

DOI: https://doi.org/10.5815/ijisa.2013.06.03

Received: 4 Sep. 2012 / Revised: 26 Nov. 2012 / Accepted: 25 Jan. 2013 / Published: 8 May 2013

Index Terms

ECG Beat Classification, SVM, PSO, Feature Selection


This paper proposes a novel system to classify three types of electrocardiogram beats, namely normal beats and two manifestations of heart arrhythmia. This system includes three main modules: a feature extraction module, a classifier module, and an optimization module. In the feature extraction module, a proper set combining the shape features and timing features is proposed as the efficient characteristic of the patterns. In the classifier module, a multi-class support vector machine (SVM)-based classifier is proposed. For the optimization module, a particle swarm optimization algorithm is proposed to search for the best value of the SVM parameters and upstream by looking for the best subset of features that feed the classifier. Simulation results show that the proposed algorithm has very high recognition accuracy. This high efficiency is achieved with only little features, which have been selected using particle swarm optimizer.

Cite This Paper

Ali Khazaee, "Heart Beat Classification Using Particle Swarm Optimization", International Journal of Intelligent Systems and Applications(IJISA), vol.5, no.6, pp.25-33, 2013. DOI:10.5815/ijisa.2013.06.03


[1]G. D. Clifford, F. Azuaje, and P. E. McShary, Advanced methods and tools for ECG data analysis, Artech House, Norwood, MA 02062, 2006.

[2]A. E. Zadeh, A. Khazaee, High Efficient System for Automatic Classification of the Electrocardiogram Beats, Ann Biomed Eng. 2011;39(3):996-1011.

[3]B. Mohammadzadeh Asl, S. K. Setarehdan, M. Mohebbi, Support vector machine-based arrhythmia classification using reduced features of heart rate variability, Artificial Intelligence in Medicine, vol. 44, 2008, pp. 51-64.

[4]A. Ebrahimzadeh, A. Khazaee, Detection of premature ventricular contractions using MLP neural networks: A comparative study, Measurement 43 (2010) 103–112.

[5]S.N. Yu, K.T. Chou, Selection of significant for ECG beat classification, Expert Syst. Appl. 36 (2009) 2088–2096.

[6]T. Ince, S. Kiranyaz, and M. Gabbouj, A Generic and Robust System for Automated Patient-Specific Classification of Electrocardiogram Signals, IEEE Trans. Biomed. Eng., vol.56, 2009, pp.1415 - 1426.

[7]A. Ebrahimzadeh, A. Khazaee, An efficient technique for classification of Electrocardiogram signals, Advances in Electrical and Computer Engineering 9 (2009) 89-93.

[8]C.H. Lin, Frequency-domain features for ECG beat discrimination using grey relational analysis-based classifier, Comput. Math. Appl., vol. 55, 2008, pp. 680–690.

[9]R.R. Sarvestani, R. Boostani, M. Roopaei, VT and VF classification using trajectory analysis, Nonlinear Anal. 2008, doi:10.1016/ j.na.2008.10.015.

[10] E. D. Ubeyli, Support vector machines for detection of electrocardiographic changes in partial epileptic Engineering Applications of Artificial Intelligence patients, vol. 21, 2008, pp. 1196-1203. 

[11] P. Chazal, M. O’Dwyer, RB. Reilly, Automatic classification of heartbeats using ECG morphology and heartbeat interval features, IEEE Trans. Biomed. Eng. vol. 51, 2004, pp.1196–1206.

[12] M. Lagerholm et al., Clustering ECG complexes using Hermite functions and self-organizing maps, IEEE Trans. Biomed. Eng., vol. 47, 2000, pp.839–847.

[13] L. Khadra, A.S. Al-Fahoum, S. Binajjaj, A quantitative analysis approach for cardiac arrhythmia classification using higher order spectral techniques, IEEE Trans. Biomed. Eng., vol. 52, 2005, pp. 1840–1845.

[14]A. Khazaee, A. Ebrahimzadeh, Classification of electrocardiogram signals with support vector machines and genetic algorithms using power spectral features, Biomedical Signal Processing and Control 5 (2010) 252–263.

[15]R. JoyMartis, C. Chakraborty, A. K. Ray, A two-stage mechanism for registration and classification of ECG using Gaussian mixture model, Pattern Recognition, 42 (2009) 2979–2988.

[16]S. Mitra, M. Mitra, B.B. Chaudhuri, A rough set-based inference engine for ECG classification, IEEE Trans. Instrum. Meas. 55 (2006) 2198–2206.

[17]F. de Chazal, R.B. Reilly, A patient adapting heart beat classifier using ECG morphology and heartbeat interval features, IEEE Trans. Biomed. Eng. 53 (2006) 2535–2543.

[18]S. Osowski, T. Markiewicz, L. T. Hoai, Recognition and classification system of arrhythmia using ensemble of neural networks, Measurement, vol. 41, 2008, pp. 610–617.

[19]A. Ebrahimzadeh, A. Khazaee, V. Ranaee, Classification of the electrocardiogram signals using supervised classifiers and efficient features, computer methods and programs in biomedicine 99 (2010) 179–194.

[20]Mohamed EA, Abdelaziz AY, Mostafa AS. A neural network-based scheme for fault diagnosis of power transformers. Electric Power Systems Research 2005;75(1):29–39.

[21]Vapnik V. The nature of statistical learning theory. New York: Springer-Verlag;1995.

[22]Huang CL, Wang CJ. A GA-based attribute selection and parameter optimization for support vector machine. Expert Syststems With Applications 2006;31(2):231–40.

[23]M. A. Al-Alaoui, A unified analog and digital design to peak and valley detector, window peak and valley detectors, and zero crossing detectors, IEEE Transactions on Instrumentation and Measurement, vol. 35, pp. 304-307, 1986.

[24]C. Cortes, and V. Vapnic, Support Vector Network, Machine Learning, vol. 20, pp.1-25, 1995.

[25]C. Burges A Tutorial on Support Vector Machines for Pattern Recognition, Data Mining and Knowledge Discovery, vol. 2, pp 121-167,1998.

[26]B. Scholkopf, C. Burges, and V. Vapnik, Extracting support data for a given task, ICKDDM, pp. 252-257,1995.

[27]Eberhart RC, Kennedy J. A new optimizer using particle swarm theory. In: Proc. ISMMH S. 1995. p. 39–43. 

[28]Shi YH, Eberhart RC. Empirical study of particle swarm optimization. In: Proceedings of the congress on evolutionary computation. 1999. p. 1945–50.

[29]R.G. Mark, and G. B. Moody, MIT-BIH Arrhythmia Database 1997 [Online]. Available: http://ecg.mit.edu/dbinfo.html

[30]G. B. Moody, and R. G. Mark, The impact of the MIT/BIH arrhythmia database,” IEEE Eng. Med. Biol. Mag., vol. 20, no. 3, pp. 45–50, May- Jun. 2001.