A Spatially Extended Model of the Human Atrioventricular Node
Paper i proceeding, 2017

The atrioventricular (AV) node plays a crucial role during many supraventricular tachycardias (SVT). To better understand its function under these complex conditions, mathematical modelling has emerged as a valuable tool. The model presented here builds on a recently published 1D model of the human AV-node, consisting of a series of interacting nodes, each with separate dynamics in refractory time and conduction delay. Here, we extend the formulation to 2D and demonstrate its ability to reproduce clinical data. Subsequently, we use it to study how AV-nodal properties for clinically assessed single and dual AV-node physiology affect activation for regular and stochastic input. In particular we study the effect of functional gradients within the AV node on ventricular response during atrial pacing and atrial fibrillation. Simulation results display important emergent features such as pathway switching and concealed conduction, and show differences in AF response that are not present in response to pacing. Simulation of a single impulse takes around 30 ms, admitting interactive use on clinical time scales as well as parameter estimation and uncertainty quantification. To our knowledge, the presented model is the first spatially extended human AV-node model, and as such represents a novel tool for understanding the human AV-nodal function in both healthy and diseased individuals, thereby paving the way for improved SVT diagnosis and therapy.


Mikael Wallman

Stiftelsen Fraunhofer-Chalmers Centrum för Industrimatematik

Frida Sandberg

Lunds universitet


0276-6574 (ISSN)

Vol. 44

44th Computing in Cardiology Conference (CinC)
Rennes, France,


Farmaceutisk vetenskap

Bioinformatik (beräkningsbiologi)

Sannolikhetsteori och statistik



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