title: Premature Ventricular beat classification using a dynamic Bayesian Network.
abstract: This paper investigates the viability of using the dynamic Bayesian Network framework as a tool to classify heart beats in long term ECG records. A Decision Support System composed by two layers is considered. The first layer performs the segmentation of each heartbeat available in the ECG record, whereas the second layer classifies the heartbeat as Premature Ventricular Contraction (PVC) or Other. The use of both static and dynamic Bayesian Networks is evaluated through using the records available in the MIT-BIH database, and the results show that the Dynamic one performs better, obtaining 95% of sensitivity and 98% of positive predictivity, showing that to consider the temporal relation among events is a good strategy to increase the certainty about present events.
authors.affiliation: Science e Technology Institute, Federal University of Vales do Jequitinhonha e Mucuri, Teófilo Otoni, Minas Gerais, Brazil. lorenasco@yahoo.com.br
authors.forename: Lorena S C
authors.initials: LS
authors.lastname: de Oliveira
authors.forename: Rodrigo V
authors.initials: RV
authors.lastname: Andreão
authors.forename: Mario
authors.initials: M
authors.lastname: Sarcinelli-Filho
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004562:Electrocardiography / Q000379:methods*; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D018879:Ventricular Premature Complexes / Q000175:diagnosis*