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 West is employed in comparing classifiers to select the optimum model. The system is evaluated using noisy ECG data, to simulate a realworld environment. It is hoped that the system can be further developed and fine-tuned for practical application.
| Original language | English (Ireland) |
|---|---|
| Title of host publication | Proceedings of the International Joint Conference on Neural Networks (IJCNN), Vols 1-5 |
| Publisher | IEEE |
| Number of pages | 5 |
| ISBN (Electronic) | 1098-7576 |
| ISBN (Print) | 1098-7576 |
| Publication status | Published - 1 Jan 2005 |
Authors (Note for portal: view the doc link for the full list of authors)
- Authors
- Gao, DY;Madden, M;Chambers, D;Lyons, G