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Evaluation of a Rapid Anisotropic Model for ECG Simulation

Overview of attention for article published in Frontiers in Physiology, May 2017
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Title
Evaluation of a Rapid Anisotropic Model for ECG Simulation
Published in
Frontiers in Physiology, May 2017
DOI 10.3389/fphys.2017.00265
Pubmed ID
Authors

Simone Pezzuto, Peter Kal'avský, Mark Potse, Frits W. Prinzen, Angelo Auricchio, Rolf Krause

Abstract

State-of-the-art cardiac electrophysiology models that are able to deliver physiologically motivated activation maps and electrocardiograms (ECGs) can only be solved on high-performance computing architectures. This makes it nearly impossible to adopt such models in clinical practice. ECG imaging tools typically rely on simplified models, but these neglect the anisotropic electric conductivity of the tissue in the forward problem. Moreover, their results are often confined to the heart-torso interface. We propose a forward model that fully accounts for the anisotropic tissue conductivity and produces the standard 12-lead ECG in a few seconds. The activation sequence is approximated with an eikonal model in the 3d myocardium, while the ECG is computed with the lead-field approach. Both solvers were implemented on graphics processing units and massively parallelized. We studied the numerical convergence and scalability of the approach. We also compared the method to the bidomain model in terms of ECGs and activation maps, using a simplified but physiologically motivated geometry and 6 patient-specific anatomies. The proposed methods provided a good approximation of activation maps and ECGs computed with a bidomain model, in only a few seconds. Both solvers scaled very well to high-end hardware. These methods are suitable for use in ECG imaging methods, and may soon become fast enough for use in interactive simulation tools.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 35 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 35 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 26%
Student > Doctoral Student 5 14%
Professor 3 9%
Student > Ph. D. Student 3 9%
Student > Master 2 6%
Other 5 14%
Unknown 8 23%
Readers by discipline Count As %
Engineering 10 29%
Agricultural and Biological Sciences 3 9%
Biochemistry, Genetics and Molecular Biology 2 6%
Mathematics 2 6%
Computer Science 1 3%
Other 5 14%
Unknown 12 34%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 02 May 2017.
All research outputs
#20,418,183
of 22,968,808 outputs
Outputs from Frontiers in Physiology
#9,441
of 13,720 outputs
Outputs of similar age
#270,461
of 310,760 outputs
Outputs of similar age from Frontiers in Physiology
#191
of 258 outputs
Altmetric has tracked 22,968,808 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,720 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.6. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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