Orthogonal Matching Pursuit Based Classifier for Premature Ventricular Contraction Detection

  • Pavel Dohnálek
  • Petr Gajdoš
  • Tomáš Peterek
  • Lukáš Zaorálek
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 239)


Premature Ventricular Contractions (PVCs) are a common topic of discussion among cardiologists as this type of heart arrhythmia is very frequent among the general population, often endangering people’s health. In this paper, a software system is proposed that differentiates PVCs from normal, healthy heartbeats collected in the MIT-BIH Arrhythmia Database. During classification, training data were recorded from subjects different than those from which testing data were measured, making the classifiers attempt to recognize patterns they were not trained for. A modification of the Orthogonal Matching Pursuit (OMP) based classifier is described and used for comparison with other, well-established classifiers. The absolute accuracy of the described algorithm is 87.58%. More elaboration on the results based on cross-reference is also given.


orthogonal matching pursuit electrocardiogram heart arrhythmia pattern matching sparse approximation 


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  1. 1.
    Haibing, Q., Xiongfei, L., Chao, P.: A method of continuous wavelet transform for qrs wave detection in ecg signal. In: 2010 International Conference on Intelligent Computation Technology and Automation (ICICTA), vol. 1, pp. 22–25 (2010)Google Scholar
  2. 2.
    Huptych, M., Lhotsk, L.: Proposal of feature extraction from wavelet packets decomposition of qrs complex for normal and ventricular ecg beats classification. In: Vander Sloten, J., Verdonck, P., Nyssen, M., Haueisen, J. (eds.) ECIFMBE 2008. IFMBE Proceedings, vol. 22, pp. 402–405. Springer, Heidelberg (2009)Google Scholar
  3. 3.
    Inan, O., Giovangrandi, L., Kovacs, G.T.A.: Robust neural-network-based classification of premature ventricular contractions using wavelet transform and timing interval features. IEEE Transactions on Biomedical Engineering 53(12), 2507–2515 (2006)CrossRefGoogle Scholar
  4. 4.
    Loh, W.-Y.: Classification and regression trees. Wiley Interdisc. Rew.: Data Mining and Knowledge Discovery 1(1), 14–23 (2011)CrossRefGoogle Scholar
  5. 5.
    Bortolan, G., Jekova, I., Christov, I.: Comparison of four methods for premature ventricular contraction and normal beat clustering. In: Computers in Cardiology, pp. 921–924 (2005)Google Scholar
  6. 6.
    Jang, J.-S.R., Sun, C.-T.: Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence. Prentice-Hall, Inc., Upper Saddle River (1997)Google Scholar
  7. 7.
    Gharaviri, A., Dehghan, F., Teshnelab, M., Moghaddam, H.: Comparison of neural network, anfis, and svm classifiers for pvc arrhythmia detection. In: 2008 International Conference on Machine Learning and Cybernetics, vol. 2, pp. 750–755 (2008)Google Scholar
  8. 8.
    Lavanya, D., Rani, D.K.: Performance evaluation of decision tree classifiers on medical datasets. International Journal of Computer Applications 26(4), 1–4 (2011)CrossRefGoogle Scholar
  9. 9.
    Dabney, A.R., Storey, J.D.: Optimality driven nearest centroid classification from genomic data. PloS One 2(10) (2007)Google Scholar
  10. 10.
    Gajdos, P., Moravec, P., Snasel, V.: Preprocessing methods for svd-based iris recognition. In: 2010 International Conference on Computer Information Systems and Industrial Management Applications (CISIM), pp. 48–53 (October 2010)Google Scholar
  11. 11.
    Frolov, A., Husek, D., Bobrov, P.: Brain-computer interface: Common tensor discriminant analysis classifier evaluation. In: 2011 Third World Congress on Nature and Biologically Inspired Computing (NaBIC), pp. 614–620 (2011)Google Scholar
  12. 12.
    Blumensath, T., Davies, M.E.: On the difference between Orthogonal Matching Pursuit and Orthogonal Least Squares. University of Edinburgh. Tech. Rep. (March 2007)Google Scholar
  13. 13.
    Wright, J., Yang, A.Y., Ganesh, A., Sastry, S.S., Ma, Y.: Robust face recognition via sparse representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 31, 210–227 (2009)CrossRefGoogle Scholar
  14. 14.
    Goldberger, A.L., Amaral, L.A.N., Glass, L., Hausdorff, J.M., Ivanov, P.C., Mark, R.G., Mietus, J.E., Moody, G.B., Peng, C.-K., Stanley, H.E.: PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation 101(23), 215–220 (2000)CrossRefGoogle Scholar
  15. 15.
    Moody, G., Mark, R.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001)CrossRefGoogle Scholar
  16. 16.
    Moody, G., Mark, R.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: Proceedings of the Computers in Cardiology 1990, pp. 185–188 (1990)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Pavel Dohnálek
    • 1
    • 2
  • Petr Gajdoš
    • 1
    • 2
  • Tomáš Peterek
    • 2
  • Lukáš Zaorálek
    • 1
    • 2
  1. 1.Department of Computer ScienceVŠB - Technical University of OstravaOstravaCzech Republic
  2. 2.IT4 Innovations, Centre of ExcellenceVŠB - Technical University of OstravaOstravaCzech Republic

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