Bayesian ANN Classifier for ECG Arrhythmia Diagnostic System: A Comparison Study
Lyons, Gerard J.
Madden, Michael G.
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"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¿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.