EVOLUTIONARY COMPUTATION APPROACH TO ECG SIGNAL CLASSIFICATION

Farid Melgani, Yakoub Bazi

Abstract

In this paper, we propose a novel classification system for ECG signals based on particle swarm optimization (PSO). The main objective of this system is to optimize the performance of the support vector machine (SVM) classifier in terms of accuracy by automatically: i) searching for the best subset of features where to carry out the classification task; and ii) solving the SVM model selection issue. Experiments conducted on the basis of ECG data from the MIT-BIH arrhythmia database to classify five kinds of abnormal waveforms and normal beats confirm the effectiveness of the proposed PSO-SVM classification system.

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Paper Citation


in Harvard Style

Melgani F. and Bazi Y. (2008). EVOLUTIONARY COMPUTATION APPROACH TO ECG SIGNAL CLASSIFICATION . In Proceedings of the First International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2008) ISBN 978-989-8111-18-0, pages 19-24. DOI: 10.5220/0001060200190024


in Bibtex Style

@conference{biosignals08,
author={Farid Melgani and Yakoub Bazi},
title={EVOLUTIONARY COMPUTATION APPROACH TO ECG SIGNAL CLASSIFICATION},
booktitle={Proceedings of the First International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2008)},
year={2008},
pages={19-24},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001060200190024},
isbn={978-989-8111-18-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the First International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2008)
TI - EVOLUTIONARY COMPUTATION APPROACH TO ECG SIGNAL CLASSIFICATION
SN - 978-989-8111-18-0
AU - Melgani F.
AU - Bazi Y.
PY - 2008
SP - 19
EP - 24
DO - 10.5220/0001060200190024