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Authors: G. de Lannoy ; D. François ; J. Delbeke and M. Verleysen

Affiliation: Université catholique de Louvain, Belgium

ISBN: 978-989-674-018-4

Keyword(s): Heart beat classification, AAMI standards, Support vector machines, Unbalanced data, Feature selection, Inter-patient classification.

Related Ontology Subjects/Areas/Topics: Applications and Services ; Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Computer Vision, Visualization and Computer Graphics ; Medical Image Detection, Acquisition, Analysis and Processing ; Physiological Processes and Bio-Signal Modeling, Non-Linear Dynamics ; Soft Computing

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 c lassification 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. (More)

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Paper citation in several formats:
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

@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},
}

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

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