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Computational Intelligence and Neuroscience
Volume 2011 (2011), Article ID 643816, 9 pages
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.


Supervised and interpatient 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 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.