title: The use of bayesian networks for heart beat classification.
abstract: This work proposes to use a static Bayesian network as a tool to support medical decision in the on-line detection of Premature Ventricular Contraction beats (PVC) in electrocardiogram (ECG) records, which is a well known cardiac arrhythmia available for study in standard ECG databases. The main motivation to use Bayesian networks is their capability to deal with the uncertainty embedded in the problem (the medical reasoning itself frequently embeds some uncertainty). Indeed, the probabilistic inference is quite suitable to model this kind of problem, for considering its random character; as a consequence, random variables are used to propagate the uncertainty embedded in the problem. Some topologies of static Bayesian networks are implemented and tested in this work, in order to find out the one more suitable to the problem addressed. The results of such tests are discussed in details along the text, and the conclusions are highlighted.
authors.affiliation: Graduate Program on Electrical Engineering, Federal University of Espírito Santo, Vitória, ES, Brazil.
authors.forename: Lorena Sophia Campos
authors.initials: LS
authors.lastname: de Oliveira
authors.forename: Rodrigo Varejão
authors.initials: RV
authors.lastname: Andreão
authors.forename: Mário
authors.initials: M
authors.lastname: Sarcinelli-Filho
mesh_terms: D000818:Animals; D001499:Bayes Theorem; D004562:Electrocardiography / Q000379:methods; D006339:Heart Rate / Q000502:physiology*; D006801:Humans; D016571:Neural Networks, Computer; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D018879:Ventricular Premature Complexes / Q000503:physiopathology