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

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 backpropagation 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.