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


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.


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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

author={G. Doquire and G. de Lannoy and D. François and M. Verleysen},
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2011)},

in EndNote Style

JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2011)
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