Bayesian ANN classifier for ECG arrhythmia diagnostic system: A comparison study: A comparison study

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49 Citations (Scopus)

Abstract

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.

Original languageEnglish (Ireland)
Title of host publicationProceedings. 2005 IEEE International Joint Conference on Neural Networks,
Pages2383-2388
Number of pages6
Publication statusPublished - 1 Jan 2005
EventInternational Joint Conference on Neural Networks, IJCNN 2005 - Montreal, QC, Canada
Duration: 31 Jul 20054 Aug 2005

Publication series

Name4

Conference

ConferenceInternational Joint Conference on Neural Networks, IJCNN 2005
Country/TerritoryCanada
CityMontreal, QC
Period31/07/054/08/05

Authors (Note for portal: view the doc link for the full list of authors)

  • Authors
  • Gao, D.; Madden, M.; Chambers, D.; Lyons, G.;

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