Prediction of paroxysmal atrial fibrillation

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Abstract

We present a novel method for prediction of the onset of a spontaneous (paroxysmal) atrial fibrilation episode by representing the electrocardiograph (ECG) output as two time series corresponding to the interbeat intervals and the lengths of the atrial component of the ECG. We will then show how different entropy measures can be calulated from both of these series and then combined in a neural network trained using the Bayesian evidence procedure to form and effective predictive classifier.

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Publication date2005
Publication titleProceedings of the 2nd International Conference on Computational Intelligence in Medicine and Healthcare (CIMED2005)
Place of PublicationPlymouth
PublisherBIOPATTERN Network of Excellence
Pages376-382
Number of pages7
Original languageEnglish
EventSecond International Conference on Computational Intelligence in Medicine and Healthcare -

Conference

ConferenceSecond International Conference on Computational Intelligence in Medicine and Healthcare
Period1/01/051/01/05

Bibliographic note

Second International Conference on Computational Intelligence in Medicine and Healthcare (CIMED2005), Lisbon (PT), 29 June - 1 July 2005.

    Keywords

  • prediction, spontaneous, paroxysmal, atrial fibrilation, electrocardiograph, interbeat intervals, atrial component

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