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Characterisation of human AV-nodal properties using a network model

Wallman, Mikael and Sandberg, Frida LU (2017) In Medical & Biological Engineering & Computing
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... (More)
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. (Less)
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organization
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type
Contribution to journal
publication status
epub
subject
in
Medical & Biological Engineering & Computing
publisher
Springer
ISSN
0140-0118
DOI
10.1007/s11517-017-1684-0
language
English
LU publication?
yes
id
475ae2e4-cd7b-49e0-8883-9b001430a094
date added to LUP
2017-08-09 14:43:17
date last changed
2017-08-11 03:00:02
@article{475ae2e4-cd7b-49e0-8883-9b001430a094,
  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. },
  author       = {Wallman, Mikael and Sandberg, Frida},
  issn         = {0140-0118},
  language     = {eng},
  publisher    = {Springer},
  series       = {Medical & Biological Engineering & Computing},
  title        = {Characterisation of human AV-nodal properties using a network model},
  url          = {http://dx.doi.org/10.1007/s11517-017-1684-0},
  year         = {2017},
}