Publication

Bayesian ANN Classifier for ECG Arrhythmia Diagnostic System: A Comparison Study

Lyons, Gerard J.
; 
Chambers, Des
; 
Madden, Michael G.
; 
Gao, Dayong
Thumbnail Image
Repository DOI
Publication Date
2005
Keywords
Arrhythmia, ECG signals, Bayesian artificial neural network (ANN) classifier, Decision trees, Logistic regression
Type
Conference Paper
Downloads
Show all metadata
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
cbn
Collections
Computer Science (Conference Papers)