Proceedings of the International Joint Conference on Neural Networks (IJCNN), Vols 1-5

Gerard Lyons

Proceedings of the International Joint Conference on Neural Networks (IJCNN), Vols 1-5

Gerard Lyons

Computer Science

Research output: Chapter in Book or Conference Publication/Proceeding › Conference Publication › peer-review

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	Bayesian ANN classifier for ECG arrhythmia diagnostic system: A comparison study
Number of pages	6
Publication status	Published - 1 May 2005

Authors (Note for portal: view the doc link for the full list of authors)

Authors
Gao, DY,Madden, M,Chambers, D,Lyons, G,IEEE

Cite this

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title = "Proceedings of the International Joint Conference on Neural Networks (IJCNN), Vols 1-5",

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.",

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AB - 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.

M3 - Conference Publication

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ER -

Proceedings of the International Joint Conference on Neural Networks (IJCNN), Vols 1-5

Abstract

Authors (Note for portal: view the doc link for the full list of authors)

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