Bayesian ANN Classifier for ECG Arrhythmia Diagnostic System: A Comparison Study
Lyons, Gerard J. ; Chambers, Des ; Madden, Michael G. ; Gao, Dayong
Lyons, Gerard J.
Chambers, Des
Madden, Michael G.
Gao, Dayong
Loading...
Repository DOI
Publication Date
2005
Type
Conference Paper
Downloads
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.
Abstract
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.
Funder
Publisher
Publisher DOI
Rights
Attribution-NonCommercial-NoDerivs 3.0 Ireland