Sam Coveney
Bayesian Calibration of Electrophysiology Models Using Restitution Curve Emulators
Coveney, Sam; Corrado, Cesare; Oakley, Jeremy E.; Wilkinson, Richard D.; Niederer, Steven A.; Clayton, Richard H.
Authors
Cesare Corrado
Jeremy E. Oakley
RICHARD WILKINSON r.d.wilkinson@nottingham.ac.uk
Professor of Statistics
Steven A. Niederer
Richard H. Clayton
Contributors
RICHARD WILKINSON r.d.wilkinson@nottingham.ac.uk
Project Leader
Abstract
Calibration of cardiac electrophysiology models is a fundamental aspect of model personalization for predicting the outcomes of cardiac therapies, simulation testing of device performance for a range of phenotypes, and for fundamental research into cardiac function. Restitution curves provide information on tissue function and can be measured using clinically feasible measurement protocols. We introduce novel “restitution curve emulators” as probabilistic models for performing model exploration, sensitivity analysis, and Bayesian calibration to noisy data. These emulators are built by decomposing restitution curves using principal component analysis and modeling the resulting coordinates with respect to model parameters using Gaussian processes. Restitution curve emulators can be used to study parameter identifiability via sensitivity analysis of restitution curve components and rapid inference of the posterior distribution of model parameters given noisy measurements. Posterior uncertainty about parameters is critical for making predictions from calibrated models, since many parameter settings can be consistent with measured data and yet produce very different model behaviors under conditions not effectively probed by the measurement protocols. Restitution curve emulators are therefore promising probabilistic tools for calibrating electrophysiology models.
Citation
Coveney, S., Corrado, C., Oakley, J. E., Wilkinson, R. D., Niederer, S. A., & Clayton, R. H. (2021). Bayesian Calibration of Electrophysiology Models Using Restitution Curve Emulators. Frontiers in Physiology, 12, Article 693015. https://doi.org/10.3389/fphys.2021.693015
Journal Article Type | Article |
---|---|
Acceptance Date | Jun 28, 2021 |
Online Publication Date | Jul 22, 2021 |
Publication Date | Jul 22, 2021 |
Deposit Date | Oct 21, 2021 |
Publicly Available Date | Oct 21, 2021 |
Journal | Frontiers in Physiology |
Electronic ISSN | 1664-042X |
Publisher | Frontiers Media |
Peer Reviewed | Peer Reviewed |
Volume | 12 |
Article Number | 693015 |
DOI | https://doi.org/10.3389/fphys.2021.693015 |
Public URL | https://nottingham-repository.worktribe.com/output/6506410 |
Publisher URL | https://www.frontiersin.org/articles/10.3389/fphys.2021.693015/full |
Files
Bayesian Calibration
(3 Mb)
PDF
Publisher Licence URL
https://creativecommons.org/licenses/by/4.0/
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