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Bayesian Calibration of Electrophysiology Models Using Restitution Curve Emulators
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 inform...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8339909/ https://www.ncbi.nlm.nih.gov/pubmed/34366883 http://dx.doi.org/10.3389/fphys.2021.693015 |
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author | Coveney, Sam Corrado, Cesare Oakley, Jeremy E. Wilkinson, Richard D. Niederer, Steven A. Clayton, Richard H. |
author_facet | Coveney, Sam Corrado, Cesare Oakley, Jeremy E. Wilkinson, Richard D. Niederer, Steven A. Clayton, Richard H. |
author_sort | Coveney, Sam |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8339909 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-83399092021-08-06 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. Front Physiol Physiology 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. Frontiers Media S.A. 2021-07-22 /pmc/articles/PMC8339909/ /pubmed/34366883 http://dx.doi.org/10.3389/fphys.2021.693015 Text en Copyright © 2021 Coveney, Corrado, Oakley, Wilkinson, Niederer and Clayton. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Physiology Coveney, Sam Corrado, Cesare Oakley, Jeremy E. Wilkinson, Richard D. Niederer, Steven A. Clayton, Richard H. Bayesian Calibration of Electrophysiology Models Using Restitution Curve Emulators |
title | Bayesian Calibration of Electrophysiology Models Using Restitution Curve Emulators |
title_full | Bayesian Calibration of Electrophysiology Models Using Restitution Curve Emulators |
title_fullStr | Bayesian Calibration of Electrophysiology Models Using Restitution Curve Emulators |
title_full_unstemmed | Bayesian Calibration of Electrophysiology Models Using Restitution Curve Emulators |
title_short | Bayesian Calibration of Electrophysiology Models Using Restitution Curve Emulators |
title_sort | bayesian calibration of electrophysiology models using restitution curve emulators |
topic | Physiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8339909/ https://www.ncbi.nlm.nih.gov/pubmed/34366883 http://dx.doi.org/10.3389/fphys.2021.693015 |
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