CLASSIFICATION OF PREMATURE VENTRICULAR BEAT USING BAYESIAN NETWORKS

Lorena S. C. de Oliveira, Rodrigo V. Andreão, Mário Sarcinelli-Filho

Abstract

This paper presents a system based on Bayesian networks (BN) to support medical decision-making. The proposed approach is able to learn from available data, and provides an intuitive graphical interpretation of the problem, which can be easily configured by a physician. This approach is evaluated for the first time in the problem of premature ventricular contraction (PVC) detection, using a representative set of records of the MIT-BIH database. The results obtained emphasize the capability of the Bayesian network to make decisions even when the information about some symptoms or events is not complete. Moreover, the good performance obtained opens many perspectives for the use of BN to deal with beat classification.

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Paper Citation


in Harvard Style

S. C. de Oliveira L., V. Andreão R. and Sarcinelli-Filho M. (2008). CLASSIFICATION OF PREMATURE VENTRICULAR BEAT USING BAYESIAN NETWORKS . In Proceedings of the First International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2008) ISBN 978-989-8111-16-6, pages 186-191. DOI: 10.5220/0001040001860191


in Bibtex Style

@conference{healthinf08,
author={Lorena S. C. de Oliveira and Rodrigo V. Andreão and Mário Sarcinelli-Filho},
title={CLASSIFICATION OF PREMATURE VENTRICULAR BEAT USING BAYESIAN NETWORKS},
booktitle={Proceedings of the First International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2008)},
year={2008},
pages={186-191},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001040001860191},
isbn={978-989-8111-16-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the First International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2008)
TI - CLASSIFICATION OF PREMATURE VENTRICULAR BEAT USING BAYESIAN NETWORKS
SN - 978-989-8111-16-6
AU - S. C. de Oliveira L.
AU - V. Andreão R.
AU - Sarcinelli-Filho M.
PY - 2008
SP - 186
EP - 191
DO - 10.5220/0001040001860191