FEATURE RELEVANCE ASSESSMENT IN AUTOMATIC INTER-PATIENT HEART BEAT CLASSIFICATION

G. de Lannoy, D. François, J. Delbeke, M. Verleysen

2010

Abstract

Long-term ECG recordings are often required for the monitoring of the cardiac function in clinical applications. Due to the high number of beats to evaluate, inter-patient computer-aided heart beat classification is of great importance for physicians. The main difficulty is the extraction of discriminative features from the heart beat time series. The objective of this work is the assessment of the relevance of feature sets previously proposed in the literature. For this purpose, inter-patient classification of heart beats following AAMI guidelines is investigated. The class unbalance is taken into account by using a support vector machine (SVM) classifier that integrates distinct weights for the classes. The performances of the SVM model with an appropriate selection of features are better than those of previously reported inter-patient classification models. These results show that the choice of the features is of major importance, and that some usual feature sets do not serve the classification performances. In addition, the results drop significantly when the class unbalance is not taken into account, which shows that this issue must be addressed to grasp the importance of the pathological cases.

References

  1. Association for the Advancement of Medical Instrumentation (1998). Testing and reporting performance results of cardiac rhythm and st segment measurement algorithms. ANSI/AAMI EC38:1998.
  2. Chazal, P. D., O'Dwyer, M., and Reilly, R. B. (2004). Automatic classification of heartbeats using ecg morphology and heartbeat interval features. Biomedical Engineering, IEEE Transactions on, 51:1196-1206.
  3. Cheng, W. and Chan, K. (1998). Classification of electrocardiogram using hidden markov models. Engineering in Medicine and Biology Society, 1998. Proceedings of the 20th Annual International Conference of the IEEE, 1:143-146.
  4. Christov, I., Gómez-Herrero, G., Krasteva, V., Jekova, I., Gotchev, A., and Egiazarian, K. (2006). Comparative study of morphological and time-frequency ecg descriptors for heartbeat classification. Med. Eng. Phys., 28(9):876-887.
  5. Clifford, G. D., Azuaje, F., and McSharry, P. (2006). Advanced Methods And Tools for ECG Data Analysis. Artech House, Inc., Norwood, MA, USA.
  6. Franc¸ois, D. (2008). Feature selection. In Wang, J., editor, Encyclopedia of data mining and warehousing, second edition, Information Science Reference.
  7. Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., Mietus, J., Moody, G., Peng, C.-K., and Stanley, H. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation, 101(23):e215-e220.
  8. Guyon, I., Gunn, S., Nikravesh, M., and Zadeh, L. A. (2006). Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing). Springer-Verlag New York, Inc., Secaucus, NJ, USA.
  9. Lagerholm, M., Peterson, C., Braccini, G., Edenbrandt, L., and Sornmo, L. (2000). Clustering ecg complexes using hermite functions and self-organizing maps. Biomedical Engineering, IEEE Transactions on, 47(7):838-848.
  10. Melgani, F. and Bazi, Y. (2008). Classification of electrocardiogram signals with support vector machines and particle swarm optimization. Information Technology in Biomedicine, IEEE Transactions on, 12(5):667- 677.
  11. Osowski, S. and Hoai, L. (2001). Ecg beat recognition using fuzzy hybrid neural network. Biomedical Engineering, IEEE Transactions on, 48(11):1265-1271.
  12. Osowski, S., Hoai, L., and Markiewicz, T. (2004). Support vector machine-based expert system for reliable heartbeat recognition. Biomedical Engineering, IEEE Transactions on, 51(4):582-589.
  13. Park, K., Cho, B., Lee, D., Song, S., Lee, J., Chee, Y., Kim, I., and Kim, S. (2008). Hierarchical support vector machine based heartbeat classification using higher order statistics and hermite basis function. In Computers in Cardiology, 2008, pages 229-232.
  14. Platt, J. C. (1999). Fast training of support vector machines using sequential minimal optimization. In Scholkopf, B., Burges, C., and Smola, A., editors, Advances in Kernel Methods. The MIT Press.
  15. Vapnik, V. N. (1999). The Nature of Statistical Learning Theory (Information Science and Statistics). Springer.
  16. Yinggang, Z. and Qinming, H. (2006). An unbalanced dataset classification approach based on v-support vector machine. In Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on, volume 2, pages 10496-10501.
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Paper Citation


in Harvard Style

de Lannoy G., François D., Delbeke J. and Verleysen M. (2010). FEATURE RELEVANCE ASSESSMENT IN AUTOMATIC INTER-PATIENT HEART BEAT CLASSIFICATION . In Proceedings of the Third International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2010) ISBN 978-989-674-018-4, pages 13-20. DOI: 10.5220/0002690900130020


in Bibtex Style

@conference{biosignals10,
author={G. de Lannoy and D. François and J. Delbeke and M. Verleysen},
title={FEATURE RELEVANCE ASSESSMENT IN AUTOMATIC INTER-PATIENT HEART BEAT CLASSIFICATION},
booktitle={Proceedings of the Third International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2010)},
year={2010},
pages={13-20},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002690900130020},
isbn={978-989-674-018-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Third International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2010)
TI - FEATURE RELEVANCE ASSESSMENT IN AUTOMATIC INTER-PATIENT HEART BEAT CLASSIFICATION
SN - 978-989-674-018-4
AU - de Lannoy G.
AU - François D.
AU - Delbeke J.
AU - Verleysen M.
PY - 2010
SP - 13
EP - 20
DO - 10.5220/0002690900130020