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Arrhythmia Identification from ECG Signals with a Neural Network Classifier Based on a Bayesian Framework

ARAN - Access to Research at NUI Galway

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dc.contributor.author Lyons, Gerard en
dc.contributor.author Chambers, Des en
dc.contributor.author Schukat, Michael en
dc.contributor.author Madden, Michael G. en
dc.contributor.author Gao, Dayong en
dc.date.accessioned 2009-05-13T09:12:22Z en
dc.date.available 2009-05-13T09:12:22Z en
dc.date.issued 2004 en
dc.identifier.citation "Arrhythmia Identification from ECG Signals with a Neural Network Classifier Based on a Bayesian Framework" , Dayong Gao, Michael G. Madden, Michael Schukat, Des Chambers, and Gerard Lyons. Work-in-Progress track of AI-2004, the Twenty-fourth SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, December 2004. en
dc.identifier.uri http://hdl.handle.net/10379/184 en
dc.description.abstract This paper presents an ANN-based diagnostic system for arrhythmia using Neural Network Classifier with Bayesian framework by time series biosignals. The Neural Network Classifier is built by the use of logistic regression model and back propagation algorithm. The prediction per-formance in training and test phases is evaluated by the False Rate. The dual threshold method is applied to determine diagnosis strategy and suppress false alarm signals. The results show that more than 90% prediction accuracy could be obtained using the improved methods in the study. Hopefully, the system can be further developed and fine-tuned for practical application. en
dc.format application/pdf en
dc.language.iso en en
dc.subject Arrhythmia en
dc.subject ECG signals en
dc.subject Neural network en
dc.subject Bayesian framework en
dc.subject.lcsh Arrhythmia en
dc.subject.lcsh Electrocardiography en
dc.subject.lcsh Neural circuitry en
dc.subject.lcsh Bayesian field theory en
dc.title Arrhythmia Identification from ECG Signals with a Neural Network Classifier Based on a Bayesian Framework en
dc.type Conference Paper en

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