Key result
A Dynamic Bayesian Network with a probability threshold of 0.75 achieved 99% sensitivity and positive predictive value for classifying premature ventricular beats in electrocardiograms.
Why the study?
Does a Dynamic Bayesian Network approach accurately classify premature ventricular beats in electrocardiograms?
Does a Dynamic Bayesian Network approach accurately classify premature ventricular beats in electrocardiograms?
A Dynamic Bayesian Network approach demonstrates high accuracy (99% sensitivity and PPV) for classifying premature ventricular beats in ECGs.
Should not yet change PVC detection practice; leaves open prospective validation of dynamic Bayesian networks.
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.
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Oliveira et al. (2016) studied Cardiac arrhythmias. Dynamic Bayesian Network (BN) approach was evaluated on Sensitivity and Positive Predictive Value for Premature Ventricular Beats (PVC). A Dynamic Bayesian Network with a probability threshold of 0.75 achieved 99% sensitivity and positive predictive value for classifying premature ventricular beats in electrocardiograms.
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