dc.contributor.author | Coveney, S | |
dc.contributor.author | Roney, CH | |
dc.contributor.author | Corrado, C | |
dc.contributor.author | Wilkinson, RD | |
dc.contributor.author | Oakley, JE | |
dc.contributor.author | Niederer, SA | |
dc.contributor.author | Clayton, RH | |
dc.date.accessioned | 2024-02-02T10:56:59Z | |
dc.date.available | 2022-09-19 | |
dc.date.available | 2024-02-02T10:56:59Z | |
dc.date.issued | 2022 | |
dc.identifier.issn | 2045-2322 | |
dc.identifier.other | ARTN 16572 | |
dc.identifier.other | ARTN 16572 | |
dc.identifier.uri | https://qmro.qmul.ac.uk/xmlui/handle/123456789/94410 | |
dc.description.abstract | Models of electrical excitation and recovery in the heart have become increasingly detailed, but have yet to be used routinely in the clinical setting to guide personalized intervention in patients. One of the main challenges is calibrating models from the limited measurements that can be made in a patient during a standard clinical procedure. In this work, we propose a novel framework for the probabilistic calibration of electrophysiology parameters on the left atrium of the heart using local measurements of cardiac excitability. Parameter fields are represented as Gaussian processes on manifolds and are linked to measurements via surrogate functions that map from local parameter values to measurements. The posterior distribution of parameter fields is then obtained. We show that our method can recover parameter fields used to generate localised synthetic measurements of effective refractory period. Our methodology is applicable to other measurement types collected with clinical protocols, and more generally for calibration where model parameters vary over a manifold. | en_US |
dc.publisher | Nature Research | en_US |
dc.relation.ispartof | SCIENTIFIC REPORTS | |
dc.rights | This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. | |
dc.title | Calibrating cardiac electrophysiology models using latent Gaussian processes on atrial manifolds | en_US |
dc.type | Article | en_US |
dc.rights.holder | © 2023 The Author(s). Published by Nature Research | |
dc.identifier.doi | 10.1038/s41598-022-20745-z | |
pubs.author-url | https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000864366600005&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=612ae0d773dcbdba3046f6df545e9f6a | en_US |
pubs.issue | 1 | en_US |
pubs.notes | Not known | en_US |
pubs.publication-status | Published | en_US |
pubs.volume | 12 | en_US |
rioxxterms.funder | Default funder | en_US |
rioxxterms.identifier.project | Default project | en_US |
qmul.funder | Predicting Atrial Fibrillation Mechanisms Through Deep Learning::Medical Research Council | en_US |
qmul.funder | Predicting Atrial Fibrillation Mechanisms Through Deep Learning::Medical Research Council | en_US |