FEATURE SELECTION FOR INTER-PATIENT SUPERVISED HEART BEAT CLASSIFICATION

G. Doquire, G. de Lannoy, D. François, M. Verleysen

Abstract

Supervised and inter-patient classification of heart beats is primordial in many applications requiring long-term monitoring of the cardiac function. Several classification models able to cope with the strong class unbalance and a large variety of ECG feature sets have been proposed for this task. In practice, over 200 features are often considered and the features retained in the final model are either chosen using domain knowledge or an exhaustive search in the feature sets without evaluating the relevance of each individual feature included in the classifier. As a consequence, the results obtained by these models can be suboptimal and difficult to interpret. In this work, feature selection techniques are considered to extract optimal feature subsets for state of the art ECG classification models. The performances are evaluated on real ambulatory recordings and compared to previously reported feature choices using the same models. Results indicate that a small number of individual features actually serve the classification and that better performances can be achieved by removing useless features.

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. 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.
  4. De Lannoy, G., Francois, D., Delbeke, J., and Verleysen, M. (2010). Weighted svms and feature relevance assessment in supervised heart beat classification. Communications in Computer and Information Science (Selected and extended papers of the BIOSIGNALS2010 conference), TO APPEAR.
  5. Franc¸ois, D. (2008). Feature selection. In Wang, J., editor, Encyclopedia of data mining and warehousing, second edition, Information Science Reference. Idea Group Publishing.
  6. 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.
  7. Gomez-Verdejo, V., Verleysen, M., and Fleury, J. (2009). Information-theoretic feature selection for functional data classification. NEUROCOMPUTING, 72(16-18, Sp. Iss. SI):3580-3589.
  8. 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.
  9. Llamedo-Soria, M. and Martinez, J. (2007). An ecg classification model based on multilead wavelet transform features”. In Computers in Cardiology, volume 35.
  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. Moddemeijer, R. (1989). On estimation of entropy and mutual information of continuous distributions. Signal Processing, 16(3):233-246.
  12. Nguyen, G. H., Bouzerdoum, A., and L., P. S. (2009). Learning Pattern Classification Tasks with Imbalanced Data Sets. INTECH.
  13. Osowski, S. and Hoai, L. (2001). Ecg beat recognition using fuzzy hybrid neural network. Biomedical Engineering, IEEE Transactions on, 48(11):1265-1271.
  14. 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.
  15. 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, pages 229-232.
  16. Shannon, C. E. (1948). A mathematical theory of communication. Bell Systems Technical Journal, 27:379- 423,623-656.
  17. Steuer, R., Kurths, J., Daub, C. O., Weise, J., and Selbig, J. (2002). The mutual information: Detecting and evaluating dependencies between variables. Bioinformatics, 18(suppl 2):S231-240.
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Paper Citation


in Harvard Style

Doquire G., de Lannoy G., François D. and Verleysen M. (2011). FEATURE SELECTION FOR INTER-PATIENT SUPERVISED HEART BEAT CLASSIFICATION . In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2011) ISBN 978-989-8425-35-5, pages 67-73. DOI: 10.5220/0003163200670073


in Bibtex Style

@conference{biosignals11,
author={G. Doquire and G. de Lannoy and D. François and M. Verleysen},
title={FEATURE SELECTION FOR INTER-PATIENT SUPERVISED HEART BEAT CLASSIFICATION},
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2011)},
year={2011},
pages={67-73},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003163200670073},
isbn={978-989-8425-35-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2011)
TI - FEATURE SELECTION FOR INTER-PATIENT SUPERVISED HEART BEAT CLASSIFICATION
SN - 978-989-8425-35-5
AU - Doquire G.
AU - de Lannoy G.
AU - François D.
AU - Verleysen M.
PY - 2011
SP - 67
EP - 73
DO - 10.5220/0003163200670073