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Characterisation of human AV-nodal properties using a network model

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Abstract

Characterisation of the AV-node is an important step in determining the optimal form of treatment for supraventricular tachycardias. To integrate and analyse patient-specific measurements, mathematical modelling has emerged as a valuable tool. Here we present a model of the human AV-node, consisting of a series of interacting nodes, each with separate dynamics in refractory time and conduction delay. The model is evaluated in several scenarios, including atrial fibrillation (AF) and clinical pacing, using simulated and measured data. The model is able to replicate signals derived from clinical ECG data as well as from invasive measurements, both under AF and pacing. To quantify the uncertainty in parameter estimation, 1000 parameter sets were sampled, showing that model output similar to data corresponds to limited regions in the model parameter space. The model is the first human AV-node model to capture both spatial and temporal dynamics while being efficient enough to allow interactive use on clinical timescales, as well as parameter estimation and uncertainty quantification. As such, it fills a new niche in the current set of published models and forms a valuable tool for both understanding and clinical research.

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Acknowledgements

The authors are grateful to Drs. Arnljot Tveit and Sara Ulimoen, Baerum Hospital, Drammen, Norway, for generously sharing the RATAF database. The authors acknowledge support from the Swedish Research Council, Grant No. 621-2014-6134.

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Authors and Affiliations

  1. Fraunhofer-Chalmers Centre, Department of Systems and Data Analysis, Chalmers Science Park, 412 88, Gothenburg, Sweden

    Mikael Wallman

  2. Department of Biomedical Engineering and Center for Integrative Electrocardiology (CIEL), Lund University, Box 118, 221 00, Lund, Sweden

    Frida Sandberg

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  1. Mikael Wallman
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Correspondence to Mikael Wallman.

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Wallman, M., Sandberg, F. Characterisation of human AV-nodal properties using a network model. Med Biol Eng Comput 56, 247–259 (2018). https://doi.org/10.1007/s11517-017-1684-0

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