Skip to main content
SpringerLink
Log in

Classification of ECG beats by using a fast least square support vector machines with a dynamic programming feature selection algorithm

  • Published:
Neural Computing & Applications Aims and scope Submit manuscript

Abstract

In this paper, we present a new system for the classification of electrocardiogram (ECG) beats by using a fast least square support vector machine (LSSVM). Five feature extraction methods are comparatively examined in the 15-dimensional feature space. The dimension of the each feature set is reduced by using dynamic programming based on divergence analysis. After the preprocessing of ECG data, six types of ECG beats obtained from the MIT-BIH database are classified with an accuracy of 95.2% by the proposed fast LSSVM algorithm together with discrete cosine transform. Experimental results show that not only the fast LSSVM is faster than the standard LSSVM algorithm, but also it gives better classification performance than the standard backpropagation multilayer perceptron network.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Saxena SC, Kumar V, Hamde ST (2002) Feature extraction from ECG signals using wavelet transforms for disease diagnostics. Int J Syst Sci 33:1073–1085

    Article  Google Scholar 

  2. Maglaveras N, Stamkopoulos T, Diamantaras K, Pappas C, Strintzis M (1998) ECG pattern recognition and classification using nonlinear transformations and neural networks: a review. Int J Med Inform 52:191–208

    CAS  PubMed  Google Scholar 

  3. Osowski S, Linh TH (2001) ECG beat recognition using fuzzy hybrid neural network. IEEE Trans Biomed Eng 48(11):1265–1271

    Article  CAS  PubMed  Google Scholar 

  4. Acharya UR, Bhat PS, Iyangar SS, Rao A, Dua S (2003) Classification of heart rate data using artificial neural network and fuzzy equivalance relation. Pattern Recognit 36:61–68

    Article  Google Scholar 

  5. Sternickel K (2002) Automatic pattern recognition in ECG time series. Comput Meth Prog Biomed 68:109–115

    Article  Google Scholar 

  6. Kundu M, Nasipuri M, Basu DK (2000) Knowledge-based ECG interpretation: a critical review. Pattern Recognit 33:351–373

    Article  Google Scholar 

  7. West D, West V (2000) Model selection for a medical diagnostic decision support system: a breast cancer detection case. Artif Intell Med 20(3):183–204

    CAS  PubMed  Google Scholar 

  8. Kordylewski H, Graupe D, Liu K (2001) A novel large-memory neural network as an aid in medical diagnosis applications. IEEE Trans Inform Technol Biomed 5(3):202–209

    CAS  Google Scholar 

  9. Dokur Z, Ölmez T (2001) ECG beat classification by a novel hybrid neural network. Comput Methods Prog Biomed 66:167–181

    Article  CAS  Google Scholar 

  10. Celler BG, Chazal P (1998) Low computational cost classifiers for ECG diagnosis using neural networks. In: Proceedings of the 20th annual international conference on IEEE-EMBS, pp 1337–1340

  11. Jouny I, Hamilton P, Kanapathipillai M (1994) Adaptive wavelet representation and classification of ECG signals. In: Proceedings of the 16th annual international conference on IEEE-EMBS, paper no 48

  12. Chazal P, Celler BG (1998) Selecting a neural network structure for ECG diagnosis. In: Proceedings of the 20th annual internatioal conference on IEEE-EMBS, pp 1422–1425

  13. Paul JS, Reddy MRS, Kumar VJ (1997) Automatic detection of PVC’s using autoregressive models. In: Proceedings of the 19th annual international conference on IEEE-EMBS, pp 68–71

  14. Addison PS, Watson JN, Clegg GR, Holzer M, Sterz F, Robertson CE (2000) Evaluating arrhythmias in ECG signals using wavelet transforms. IEEE Eng Med Biol 19(5):104–109

    CAS  Google Scholar 

  15. Unser M, Aldroubi A (1996) A review of wavelets in biomedical applications. Proc IEEE 84(4):626–638

    Article  Google Scholar 

  16. Yang M, Lu W, Shyu L (1997) ECG events detection and classification using wavelet and neural networks. In: Proceedings of the 20th annual international conference on IEEE-EMBS, pp 280–281

  17. Physiobank Archieve Index, MIT-BIH Arrhythmia Database. http://www.physionet.org/physiobank/database

  18. Minami K, Nakajima H, Toyoshima T (1999) Real time discrimination of ventricular tachyarrhytmia with fourier transform neural network. IEEE Trans Biomed Eng 46:179–185

    CAS  PubMed  Google Scholar 

  19. Lang M (1998) Allpas filter design and applications. IEEE Trans Signal Process 46(9):2505–2514

    Article  Google Scholar 

  20. Cohen A (1986) Biomedical signal processing, vol 2. CRC Press Inc, Florida

  21. Daubechies I (1992) Ten lectures on wavelets. Capital city press, Montpelier

    Google Scholar 

  22. Akay M (1994) Biomedical signal processing. Academic, San Diego

    Google Scholar 

  23. Haykin S (1996) Adaptive filter theory. Prentice Hall, New Jersey

    Google Scholar 

  24. Haykin S (1999) Neural networks; a comprehensive foundation. Prentice Hall, New Jersey

    Google Scholar 

  25. Bertsekas DP (1995) Nonlinear programming. Athenas Scientific, Belmont

    Google Scholar 

  26. Suykens JAK, Wandewalle J (1999) Least squares support vector machine classifiers, neural process. Lett 9:293–300

    Google Scholar 

  27. Chua KS (2003) Efficient computations for large least square support vector machine classifiers. Pattern Recognit Lett 24:75–80

    Article  Google Scholar 

  28. Zurada JM (1992) Introduction to artificial neural systems. PWS Publishing Company, Boston

    Google Scholar 

  29. McNeil BJ, Keeler E, Adelstein SJ (1975) Primer on certain elements of medical decision making. J Med (The New England) 293:211–215

    CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Electrical and Electronics Engineering Department, Nigde University, 51200, Nigde, Turkey

    Nurettin Acır

Authors
  1. Nurettin Acır
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nurettin Acır.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Acır, N. Classification of ECG beats by using a fast least square support vector machines with a dynamic programming feature selection algorithm. Neural Comput & Applic 14, 299–309 (2005). https://doi.org/10.1007/s00521-005-0466-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-005-0466-z

Keywords

Search

Navigation