title: Detection of premature ventricular beats in ECG records using bayesian networks involving the P-Wave and fusion of results.
abstract: This article proposes to use the Bayesian network (BN) framework to support medical decision in the problem of heart beat classification in long-term electrocardiogram (ECG) records. The motivation to use the BN approach is to take into account the uncertainty present in the clinical reasoning. The case study is the classification of Premature Ventricular Beats (PVC). Specifically speaking, it is discussed the use of the P-Wave as a network node, to check its capability to improve the performance of the PVC classification. In spite of concluding that the P wave is not definitive for the classification, such results have motivated the main proposal of this work: a fusion of the results obtained by training the implemented BN with two distinct datasets, which has indeed improved the system performance.
authors.affiliation: Graduate Program on Electrical Engineering, Federal University of Espirito Santo, Vitoria, 29075-910, 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: Andreao
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*