USING SUPPORT VECTOR MACHINES (SVMS) WITH REJECT OPTION FOR HEARTBEAT CLASSIFICATION

Zahia Zidemal, Ahmed Amirou, Adel Belouchrani

Abstract

In this paper, we introduce a new system for ECG beat classification using Support Vector Machines (SVMs) classifier with a double hinge loss. This classifier has the option to reject samples that cannot be classified with enough confidence. Specifically in medical diagnoses, the risk of a wrong classification is so high that it is convenient to reject the sample. After ECG preprocessing, feature selection and extraction, our decision rule uses dynamic reject thresholds following the cost of rejecting a sample and the cost of misclassifying a sample. Significant performance enhancement is observed when the proposed approach was tested with the MIT/BIH arrythmia database. The achieved results are represented by the error reject tradeoff and a sensitivity higher than 99%, being competitive to other published studies.

References

  1. Afonso, O. and Tompkins, W. (1999). Classification of premature ventricular complexes using filter bank features, induction of decision trees and a fuzzy rulebased system. Med. Biol. Eng, 37:560-565.
  2. Bartlett, P. L. and Wegkamp, M. H. (2007). Classification with a reject option using a hinge loss. Technical Report M980, Department of Statistics, Florida State University.
  3. Chazal, P., O'Dwyer, M., and Reilly, R. (2004). Automatic classification of hearthbeats using ecg morphology and hearthbeat interval features. Trans.Biom.Eng, 51(7):1196-1206.
  4. Chow, C. K. (1957). An optimum character recognition system using decision function. IRE Trans. Electronic Computers, EC-6(4):247-254.
  5. Chow, C. K. (1970). On optimum recognition error and reject tradeoff. IEEE Trans. on Information Theory, 16(1):41-46.
  6. Cristianini, N. and Shawe-Taylor, J. (2000). An Introduction to Support Vector Machines. Cambridge University Press.
  7. Donoho, D. (1995). Denoising by soft thresholding. IEEE, Trans on Info Theory, 41(3):613-627.
  8. Fumera, G. and Roli, F. (2002). Support vector machines with embedded reject option. In Lee, S.-W. and Verri, A., editors, Pattern Recognition with Support Vector Machines: First International Workshop, Lecture Notes in Computer Science, pages 68-82. Springer.
  9. Gomez-Herrero, G., Jecova, I., Krasteva, V., and Egiazarian, K. (2006). Relative estimation of the karhunen loeve transform basis functions for detection of ventricular ectopic beats. IEEE Comput.Cardiol, 33:569- 572.
  10. Herbei, R. and Wegkamp, M. H. (2006). Classification with reject option. The Canadian Journal of Statistics, 34(4):709-721.
  11. Hu, Y. H., Palready, S. H., and Tompkins, W. J. (1997). A patient-adaptable ecg beat classifier using a mixture of experts approach. Trans.Biom.Eng, 44(4):891-900.
  12. Jecova, I., Bortolan, G., and Christov, I. (2004). Pattern recognition and optimal parameter selection in premature ventricular contraction calssification. IEEE, Computer in cardiology, 31:357-360.
  13. Krasteva, V. and Jecova, I. (2007). Qrs template matching for recognition of ventricular ectopic beats. Annals of Biomedical Engineering, 35(12):2065-2076.
  14. Kwok, J. T. (1999). Moderating the outputs of support vector machine classifiers. IEEE Trans. on Neural Networks, 10(5):1018-1031.
  15. Lagerholm, M., Peterson, G., Braccini, G., Edenbrandt, L., and Soërnmo, L. (2000). Clustering ecg complex using hermite functions and self-organizing maps. IEEE.Trans.Biom.Eng, 47:838-848.
  16. Lepage, R. (2003). Detection et analyse de l'onde p d'un electrocardiogramme: Application au dépistage de la fibrillation auriculaire. Thse de Doctorat de l'université de Bretagne Occidentale.
  17. Lin, H., Lin, C., and Weng, R. C. (2003). A note on platt's probabilistic outputs for support vector machines. Technical report, National Taiwan University, Taipei 116, Taiwan.
  18. Loosli, G., Canu, S., Vishwanathan, S., and Chattopadhay, M. (2005). Boite à outils SVM simple et rapide. RIA - Revue d'Intelligence Artificielle, 19(4/5):741-767.
  19. Minami, K., NAkajima, H., and Toyoshima, T. (1999). Real-time discrimination of ventricular tachyarrhythmia with fourier-transform neural network. Trans.Biom.Eng, 46:179-185.
  20. Osowski, S., Hoai, L, T., and Markiewicz, T. (2004). Support vector machine-based expert system for reliable heartbeat recognition. Trans.Biom.Eng, 51(4):582- 589.
  21. Pan, J. and Tompkins, W. J. (1985). A real-time qrs dtection algorithm. Trans.Biom.Eng, 32(3):230-236.
  22. Platt, J. C. (2000). Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Advances in Large Margin Classifiers.
  23. Ramaswamy, P., Cota, N. G., Chan, K. L., and Shankar, M. K. (2004). Multi-parameter detection of ectopic heartbeats. IEEE, Int.Workshop.BioCAS.
  24. Tortorella, F. (2004). Reducing the classification cost of support vector classifiers through an ROC-based reject rule. Pattern Analysis & Applications, 7(2):128-143.
  25. Vapnik, V. N. (1995). The Nature of Statistical Learning Theory. Springer Series in Statistics. Springer.
  26. Vishwanathan, S. V. N., Smola, A., and Murty, N. (2003). SimpleSVM. In Fawcett, T. and Mishra, N., editors, Proceedings of the Twentieth International Conference on Machine Learning (ICML-2003), pages 68- 82. AAAI.
  27. Yeap, T. H. (1990). Ecg beat classification by a neural network. Annual international conf of IEEE Eng in Med and Biol, 12:1457-1458.
Download


Paper Citation


in Harvard Style

Zidemal Z., Amirou A. and Belouchrani A. (2009). USING SUPPORT VECTOR MACHINES (SVMS) WITH REJECT OPTION FOR HEARTBEAT CLASSIFICATION . In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2009) ISBN 978-989-8111-65-4, pages 204-210. DOI: 10.5220/0001431602040210


in Bibtex Style

@conference{biosignals09,
author={Zahia Zidemal and Ahmed Amirou and Adel Belouchrani},
title={USING SUPPORT VECTOR MACHINES (SVMS) WITH REJECT OPTION FOR HEARTBEAT CLASSIFICATION},
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2009)},
year={2009},
pages={204-210},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001431602040210},
isbn={978-989-8111-65-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2009)
TI - USING SUPPORT VECTOR MACHINES (SVMS) WITH REJECT OPTION FOR HEARTBEAT CLASSIFICATION
SN - 978-989-8111-65-4
AU - Zidemal Z.
AU - Amirou A.
AU - Belouchrani A.
PY - 2009
SP - 204
EP - 210
DO - 10.5220/0001431602040210