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dc.contributor.authorLyons, Gerarden
dc.contributor.authorChambers, Desen
dc.contributor.authorMadden, Michael G.en
dc.contributor.authorGao, Dayongen
dc.identifier.citation"Bayesian ANN Classifier for ECG Arrhythmia Diagnostic System: A Comparison Study" , Dayong Gao, Michael G. Madden, Des Chambers, and Gerard Lyons, Proc. International Joint Conference on Neural Networks, Montreal, July 2005.en
dc.description.abstractAbstract¿This paper outlines a system for detection of cardiac arrhythmias within ECG signals, based on a Bayesian Artificial Neural Network (ANN) classifier. The Bayesian (or Probabilistic) ANN Classifier is built by the use of a logistic regression model and the back propagation algorithm based on a Bayesian framework. Its performance for this task is evaluated by comparison with other classifiers including Naive Bayes, Decision Trees, Logistic Regression, and RBF Networks. A paired t-test is employed in comparing classifiers to select the optimum model. The system is evaluated using noisy ECG data, to simulate a real-world environment. It is hoped that the system can be further developed and fine-tuned for practical application.en
dc.subjectECG signalsen
dc.subjectBayesian artificial neural network (ANN) classifieren
dc.subjectDecision treesen
dc.subjectLogistic regressionen
dc.subject.lcshBayesian field theoryen
dc.subject.lcshDecision treesen
dc.subject.lcshLogistic regression analysisen
dc.titleBayesian ANN Classifier for ECG Arrhythmia Diagnostic System: A Comparison Studyen
dc.typeConference Paperen

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