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Computational Intelligence and Neuroscience
Volume 2011, Article ID 643816, 9 pages
http://dx.doi.org/10.1155/2011/643816
Research Article

Feature Selection for Interpatient Supervised Heart Beat Classification

1Machine Learning Group, ICTEAM Institute, Catholic University of Leuven, Place du Levant 3, 1348 Louvain-la-Neuve, Belgium
2Neuroscience Institute, Catholic University of Leuven, Avenue Hippocrate 54, 1200 Bruxelles, Belgium

Received 24 February 2011; Revised 1 June 2011; Accepted 4 June 2011

Academic Editor: Saeid Sanei

Copyright © 2011 G. Doquire et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. G. de Lannoy, D. Franc, J. Delbeke, and M. Verleysen, “Weighted svms and feature relevance assessment in supervised heart beat classification,” Communications in Computer and Information Science, vol. 127, pp. 212–225, 2011, Selected and extended papers of the BIOSIGNALS 2010 conference. View at Google Scholar
  2. I. Guyon, S. Gunn, M. Nikravesh, and L. A. Zadeh, Feature Extraction: Foundations and Applications, Studies in Fuzziness and Soft Computing, Springer, New York, NY, USA, 2006.
  3. G. H. Nguyen, A. Bouzerdoum, and S. L. Phung, “Learning pattern classification tasks with imbalanced data sets,” in Pattern Recognition, P. Yin, Ed., InTech, Vukovar, Croatia, 2009. View at Google Scholar
  4. P. D. Chazal, M. O'Dwyer, and R. B. Reilly, “Automatic classification of heartbeats using ECG morphology and heartbeat interval features,” IEEE Transactions on Biomedical Engineering, vol. 51, no. 7, pp. 1196–1206, 2004. View at Publisher · View at Google Scholar · View at Scopus
  5. Association for the Advancement of Medical Instrumentation, “Testing and reporting performance results of cardiac rhythm and st segment measurement algorithms,” 1998, ANSI/AAMI EC38:1998. View at Google Scholar
  6. M. Llamedo and J. P. Martinez, “An ecg classification model based on multilead wavelet transform features,” Computers in Cardiology, vol. 34, pp. 105–108, 2007. View at Google Scholar
  7. M. Llamedo and J. P. Martinez, “Heartbeat classification using feature selection driven by database generalization criteria,” IEEE Transactions on Biomedical Engineering, vol. 58, pp. 616–625, 2011. View at Google Scholar
  8. K. Park, B. Cho, D. Lee et al., “Hierarchical support vector machine based heartbeat classification using higher order statistics and hermite basis function,” Computers in Cardiology, vol. 35, pp. 229–232, 2008. View at Google Scholar
  9. V. N. Vapnik, The Nature of Statistical Learning Theory, Information Science and Statistics, Springer, Berlin, Germany, 1999.
  10. R. Akbani, S. Kwek, and N. Japkowicz, “Applying support vector machines to imbalanced datasets,” in Proceedings of the 15th European conference on Machine Learning (ECML '04), pp. 39–50, Springer, Pisa, Italy, September 2004.
  11. J. C. Platt, “Fast training of support vector machines using sequential minimal optimization,” in Advances in Kernel Methods, The MIT Press, New York, NY, USA, 1999. View at Google Scholar
  12. C. E. Shannon, “A mathematical theory of communication,” Bell Systems Technical Journal, vol. 27, pp. 379–423, 623–656, 1948. View at Publisher · View at Google Scholar · View at Scopus
  13. F. Rossi, A. Lendasse, D. François, V. Wertz, and M. Verleysen, “Mutual information for the selection of relevant variables in spectrometric nonlinear modelling,” Chemometrics and Intelligent Laboratory Systems, vol. 80, no. 2, pp. 215–226, 2006. View at Publisher · View at Google Scholar · View at Scopus
  14. R. Moddemeijer, “On estimation of entropy and mutual information of continuous distributions,” Signal Processing, vol. 16, no. 3, pp. 233–248, 1989. View at Google Scholar · View at Scopus
  15. R. Steuer, J. Kurths, C. O. Daub, J. Weise, and J. Selbig, “The mutual information: detecting and evaluating dependencies between variables,” Bioinformatics, vol. 18, supplement 2, pp. S231–S240, 2002. View at Google Scholar · View at Scopus
  16. V. Gomez-Verdejo, M. Verleysen, and J. Fleury, “Information-theoretic feature selection for functional data classification,” Neurocomputing, vol. 72, no. 16–18, pp. 3580–3589, 2009. View at Publisher · View at Google Scholar · View at Scopus
  17. I. Guyon and A. Elisseeff, “An introduction to variable and feature selection,” Journal of Machine Learning Research, vol. 3, pp. 1157–1182, 2003. View at Google Scholar
  18. R. Kohavi and G. H. John, “Wrappers for feature subset selection,” Artificial Intelligence, vol. 97, no. 1-2, pp. 273–324, 1997. View at Google Scholar · View at Scopus
  19. I. Guyon, J. Weston, S. Barnhill, and V. Vapnik, “Gene selection for cancer classification using support vector machines,” Machine Learning, vol. 46, no. 1-3, pp. 389–422, 2002. View at Publisher · View at Google Scholar · View at Scopus
  20. K. Kira and L. A. Rendell, “The feature selection problem: traditional methods and a new algorithm,” in Proceedings of the tenth National Conference on Artificial intelligence, (AAAI '92), pp. 129–134, AAAI Press, 1992.
  21. P. E. Meyer, C. Schretter, and G. Bontempi, “Information-theoretic feature selection in microarray data using variable complementarity,” IEEE Journal on Selected Topics in Signal Processing, vol. 2, no. 3, pp. 261–274, 2008. View at Publisher · View at Google Scholar · View at Scopus
  22. F. Fleuret, “Fast binary feature selection with conditional mutual information,” Journal of Machine Learning Research, vol. 5, no. 4941, pp. 1531–1555, 2004. View at Google Scholar
  23. H. Peng, F. Long, and C. Ding, “Feature selection based on mutual information: criteria of max-dependency, max-Relevance, and min-redundancy,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 8, pp. 1226–1238, 2005. View at Publisher · View at Google Scholar · View at Scopus
  24. A. L. Goldberger, L. A. Amaral, L. Glass et al., “PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals,” Circulation, vol. 101, no. 23, pp. e215–e220, 2000. View at Google Scholar · View at Scopus
  25. I. Christov, G. Gómez-Herrero, V. Krasteva, I. Jekova, A. Gotchev, and K. Egiazarian, “Comparative study of morphological and time-frequency ECG descriptors for heartbeat classification,” Medical Engineering and Physics, vol. 28, no. 9, pp. 876–887, 2006. View at Publisher · View at Google Scholar · View at Scopus
  26. F. Melgani and Y. Bazi, “Classification of electrocardiogram signals with support vector machines and particle swarm optimization,” IEEE Transactions on Information Technology in Biomedicine, vol. 12, no. 5, pp. 667–677, 2008. View at Google Scholar
  27. M. Lagerholm, C Peterson, G. Braccini, L. Edenbrandt, and L. Sornmo, “Clustering ecg complexes using hermite functions and self-organizing maps,” IEEE Transactions on Biomedical Engineering, vol. 47, no. 7, pp. 838–848, 2000. View at Google Scholar
  28. S. Osowski, L. Hoai, and T. Markiewicz, “Support vector machine-based expert system for reliable heartbeat recognition,” IEEE Transactions on Biomedical Engineering, vol. 51, no. 4, pp. 582–589, 2004. View at Google Scholar
  29. S. Osowski and L. Hoai, “ECG beat recognition using fuzzy hybrid neural network,” IEEE Transactions on Biomedical Engineering, vol. 48, no. 11, pp. 1265–1271, 2001. View at Publisher · View at Google Scholar · View at Scopus