Bayesian Network with Decision Threshold for Heart Beat Classification


This work proposes a Dynamic Bayesian Network approach (BN) to support medical call making in the matter of beat classification in electrocardiograms. The BN takes the uncertainty into consideration when making a call each time new proof is available. Moreover, the understanding connected to the beat classification can be controlled through a threshold adjusted by the specialist. The performance of the BN primarily based classifier is assessed through the MIT-BIH database, considering two beat classes: Premature Ventricular Beat (PVC category) and Different (gathering all different beat categories). The BN with chance threshold of zero.seventy five achieved ample Sensitivity and Positive Predictive of 99% for the PVC beats. The results show that the BN framework could be a promising tool for classifying cardiac arrhythmias.

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