Signal, Image and Video Processing

, Volume 8, Issue 5, pp 931–942 | Cite as

Detection of premature ventricular contraction arrhythmias in electrocardiogram signals with kernel methods

  • Naif Alajlan
  • Yakoub Bazi
  • Farid Melgani
  • Salim Malek
  • Mohamed A. Bencherif
Original Paper


In this paper, we propose to investigate the capabilities of two kernel methods for the detection and classification of premature ventricular contractions (PVC) arrhythmias in Electrocardiogram (ECG signals). These kernel methods are the support vector machine and Gaussian process (GP). We propose to study these two classifiers with various feature representations of ECG signals, such as morphology, discrete wavelet transform, higher-order statistics, and S transform. The experimental results obtained on 48 records (i.e., 109,887 beats) of the MIT-BIH Arrhythmia database showed that for all feature representation adopted in this work, the GP detector trained only with 600 beats from PVC and Non-PVC classes can provide an overall accuracy and a sensitivity above 90 % on 20 records (i.e., 49,774 beats) and 28 records (i.e., 60,113 beats) seen and unseen, respectively, during the training phase.


Premature ventricular contraction (PVC) Support vector machines (SVMs) Gaussian process classifiers (GPCs) Morphology Wavelet transform High-order statistics S transform 


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  1. 1.
    Melgani F., Bazi Y.: Classification of electrocardiogram signals with support vector machines and swarm particle optimization. In: IEEE Trans. Inf. Technol. Biomed. 12(5), 667–677 (2008)Google Scholar
  2. 2.
    Ceylan R., Ozbay Y., Karlik B.: A novel approach for classification of ECG arrhythmias: type-2 fuzzy clustering neural network. Expert Syst. Appl. 36(3), 6721–6726 (2009)CrossRefGoogle Scholar
  3. 3.
    Martis R.J., Chakraborty C.C., Ray A.K.: A two-stage mechanism for registration and classification of ECG using Gaussian mixture model. Pattern Recognit. 42(11), 2979–2988 (2009)CrossRefzbMATHGoogle Scholar
  4. 4.
    Ubeyli E.D.: Combining recurrent neural networks with eigenvector methods for classification of ECG beats. Digit. Signal Process. 19(2), 320–329 (2009)CrossRefGoogle Scholar
  5. 5.
    Khorrami H., Moavenian M.: A comparative study of DWT, CWT and DCT transformations in ECG arrhythmias classification. Expert Syst. Appl. 37(8), 5751–5757 (2010)CrossRefGoogle Scholar
  6. 6.
    Ozbay Y., Tezel G.: A new method for classification of ECG arrhythmias using neural network with adaptive activation function. Digit. Signal Process. 20(4), 1040–1049 (2010)CrossRefGoogle Scholar
  7. 7.
    Moavenian M., Khorrami H.: A qualitative comparison of Artificial neural networks and support vector machines in ECG arrhythmias classification. Expert Syst. Appl. 37(8), 3088–3093 (2010)CrossRefGoogle Scholar
  8. 8.
    Khazaee A., Ebrahimzadeh A.: Classification of electrocardiogram signals with support vector machines and genetic algorithms using power spectral features. Biomed. Signal Process. Control 5(4), 252–263 (2010)CrossRefGoogle Scholar
  9. 9.
    Korurek M., Dogan B.: ECG beat classification using particle swarm optimization and radial basis function neural network. Expert Syst. Appl. 37(12), 7563–7569 (2010)CrossRefGoogle Scholar
  10. 10.
    Pasolli E., Melgani F.: Active Learning methods for electrocardiographic signal classification. In: IEEE Trans. Inf. Technol. Biomed. 14(6), 1405–1416 (2010)Google Scholar
  11. 11.
    Ozbay Y., Ceylan R., Karlik B.: Integration of type-2 fuzzy clustering and wavelet transform in a neural network based ECG classifier. Expert Syst. Appl. 38(1), 1004–1010 (2011)CrossRefGoogle Scholar
  12. 12.
    Lanata A., Valenza G., Mancuso C., Scilingo E.P.: Robust multiple cardiac arrhythmia detection through bispectrum analysis. Expert Syst. Appl. 38(6), 6798–6804 (2011)CrossRefGoogle Scholar
  13. 13.
    Vapnik V.: Statistical Learning Theory. Wiley, New York (1998)zbMATHGoogle Scholar
  14. 14.
    Williams C.K.I., Barber D.: Bayesian classification with Gaussian processes. In: IEEE Trans. Pattern Anal. Mach. Intell. 20(12), 1342–1351 (1998)Google Scholar
  15. 15.
    Gibbs M., MacKay D.J.C.: Variational Gaussian process classifiers. In: IEEE Trans. Neural Netw. 11(6), 1458–1464 (2000)Google Scholar
  16. 16.
    Minka, T.P.: A family of algorithm for approximate Bayesian inference. Ph.D. thesis, Massachusetts Institute of Technology (2001)Google Scholar
  17. 17.
    Kuss M., Rasmussen C.: Assessing approximate inference for binary Gaussian process classification. J. Mach. Learn. Res. 6, 1679–1704 (2005)zbMATHMathSciNetGoogle Scholar
  18. 18.
    Rasmussen C., Williams C.K.I.: Gaussian Process for Machine dearning. The MIT Press, Cambridge, MA (2006)Google Scholar
  19. 19.
    Bazi Y., Melgani F.: Gaussian process approach to remote sensing image classification. In: IEEE Trans. Geosci. Remote Sens. 48(1), 186–197 (2010)Google Scholar
  20. 20.
    Kim H.C., Ghahramani Z.: Bayesian Gaussian process classification with the EM-EP algorithm. In: IEEE Trans. Pattern Anal. Mach. Intell. 28(12), 1948–1959 (2006)Google Scholar
  21. 21.
    Mark, R., Moody, G.: MIT-BIH Arrhythmia Database 1997 [Online]. Available at (1997)
  22. 22.
    Mallat S.G.: Multifrequency channel decompositions of image and wavelet models. In: IEEE Trans. Acoust. Speech Signal Process. 37(12), 2091–2110 (1989)Google Scholar
  23. 23.
    Nikias C., Petropulu A.: Higher Order Spectral Analysis. Prentice-Hall, Englewood Cliffs, NJ (1993)Google Scholar
  24. 24.
    Stockwell R.G., Mansinha L., Lowe R.P.: Localization of the complex spectrum: the S-transform. In: IEEE Trans. Signal Process. 44(4), 998–1001 (1996)Google Scholar
  25. 25.
    Wei J.J., Chang C.J., Shou N.K., Jan G.J.: ECG data compression using truncated singular value decomposition. In: IEEE Trans. Biomed. Eng. 5(4), 290–299 (2001)Google Scholar
  26. 26.
    Peng H., Long F., Ding C.: Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. In: IEEE Trans. Pattern Anal. Mach. Intell. 27(8), 1226–1238 (2005)Google Scholar
  27. 27.
    Stone M.: Cross-validatory choice and assessment of statistical predictions. J. R. Stat. Soc. B 36, 111–147 (1974)zbMATHGoogle Scholar
  28. 28.
    Chang, C.-C., Lin, C.-J.: LIBSVM—A Library for Support Vector Machines [Online]. Available at
  29. 29.
    Rasmussen, C.E., Williams, K.I.: Gaussian Process Software [Online]. Available at

Copyright information

© Springer-Verlag London Limited 2012

Authors and Affiliations

  • Naif Alajlan
    • 1
  • Yakoub Bazi
    • 1
  • Farid Melgani
    • 2
  • Salim Malek
    • 1
  • Mohamed A. Bencherif
    • 1
  1. 1.ALISR Laboratory, College of Computer and Information SciencesKing Saud UniversityRiyadhSaudi Arabia
  2. 2.Department of Information Engineering and Computer ScienceUniversity of TrentoTrentoItaly

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