1. Bayesian ANN classifier for ECG arrhythmia diagnostic system: a comparison study
- Author
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Dayong Gao, Desmond Chambers, Gerard J. Lyons, and Michael G. Madden
- Subjects
ECG signals ,Computer science ,Decision trees ,Bayesian probability ,Decision tree ,Logistic regression ,Machine learning ,computer.software_genre ,Electrocardiography ,Naive Bayes classifier ,Probabilistic classification ,Bayesian field theory ,Logistic regression analysis ,Artificial neural network ,business.industry ,Probabilistic logic ,Pattern recognition ,Regression analysis ,Backpropagation ,Bayesian artificial neural network (ANN) classifier ,ComputingMethodologies_PATTERNRECOGNITION ,Artificial intelligence ,business ,Classifier (UML) ,computer ,Arrhythmia - 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. more...
- Published
- 2006
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