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A Statistical Atrioventricular Node Model Accounting for Pathway Switching During Atrial Fibrillation

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Author

Summary, in English

Objective: The atrioventricular (AV) node plays a central role in atrial fibrillation (AF) as it influences the conduction of impulses from the atria into the ventricles. In the present paper, the statistical dual pathway AV node model, previously introduced by us, is modified so that it accounts for atrial impulse pathway switching even if the preceding impulse did not cause a ventricular activation. Methods: The proposed change in model structure implies that the number of model parameters subjected to maximum likelihood estimation is reduced from five to four. The model is evaluated using the data acquired in the RATe control in Atrial Fibrillation (RATAF) study, involving 24- h ECG recordings from 60 patients with permanent AF. Results: When fitting the models to the RATAF database, similar results were obtained for both the present and the previous model, with a median fit of 86%. The results show that the parameter estimates characterizing refractory period prolongation exhibit considerably lower variation when using the present model, a finding that may be ascribed to fewer model parameters. Conclusion: The new model maintains the capability to model RR intervals, while providing more reliable parameters estimates. Significance: The model parameters are expected to convey novel clinical information, and may be useful for predicting the effect of rate control drugs.

Publishing year

2016-09

Language

English

Pages

1842-1849

Publication/Series

IEEE Transactions on Biomedical Engineering

Volume

63

Issue

9

Document type

Journal article

Publisher

IEEE - Institute of Electrical and Electronics Engineers Inc.

Topic

  • Medical Biotechnology
  • Medical Biotechnology

Status

Published

Project

  • Multilevel Modeling of the Atrioventricular Node for Personalized Treatment of Atrial Fibrillation
  • Modelling and Quality Assessment of Atrial Fibrillatory Waves

ISBN/ISSN/Other

  • ISSN: 1558-2531