Rede Bayesiana com Limiar de Decisão para Classificação de Batimentos Cardíacos
(Bayesian Network with Decision Threshold for Heart Beat Classification)
Lorena Sophia Campos Oliveira (email@example.com)1, Rodrigo Varejão Andreão (firstname.lastname@example.org)2, Mário Sarcinelli Filho (email@example.com)3
1Universidade Federal dos Vales do Jequitinhonha e Mucuri2Instituto Federal de Educação, Ciência e Tecnologia do Espírito Santo3Universidade Federal do Espírito Santo
This paper appears in: Revista IEEE América Latina
Publication Date: March 2016
Volume: 14, Issue: 3
This work proposes a Dynamic Bayesian Network approach (BN) to support medical decision making in the problem of beat classification in electrocardiograms. The BN takes the uncertainty into consideration when making a decision every time new evidence is available. Moreover, the certainty related to the beat classification can be controlled through a threshold adjusted by the specialist. The performance of the BN based classifier is assessed through the MIT-BIH database, considering two beat classes: Premature Ventricular Beat (PVC class) and Other (gathering all other beat classes). The BN with probability threshold of 0.75 achieved scores of Sensitivity and Positive Predictive of 99% for the PVC beats. The results show that the BN framework is a promising tool for classifying cardiac arrhythmias.
Bayesian Network, Electrocardiogram, Beat classification, PVC detection
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