dc.contributor.author | Coveney, S | |
dc.contributor.author | Cantwell, C | |
dc.contributor.author | Roney, C | |
dc.date.accessioned | 2024-02-02T10:27:23Z | |
dc.date.available | 2022-06-07 | |
dc.date.available | 2024-02-02T10:27:23Z | |
dc.date.issued | 2022 | |
dc.identifier.issn | 0140-0118 | |
dc.identifier.uri | https://qmro.qmul.ac.uk/xmlui/handle/123456789/94406 | |
dc.description.abstract | Characterizing patient-specific atrial conduction properties is important for understanding arrhythmia drivers, for predicting potential arrhythmia pathways, and for personalising treatment approaches. One metric that characterizes the health of the myocardial substrate is atrial conduction velocity, which describes the speed and direction of propagation of the electrical wavefront through the myocardium. Atrial conduction velocity mapping algorithms are under continuous development in research laboratories and in industry. In this review article, we give a broad overview of different categories of currently published methods for calculating CV, and give insight into their different advantages and disadvantages overall. We classify techniques into local, global, and inverse methods, and discuss these techniques with respect to their faithfulness to the biophysics, incorporation of uncertainty quantification, and their ability to take account of the atrial manifold. | en_US |
dc.format.extent | 2463 - 2478 | |
dc.publisher | Springer Nature | en_US |
dc.relation.ispartof | MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING | |
dc.rights | Open Access 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 | Atrial conduction velocity mapping: clinical tools, algorithms and approaches for understanding the arrhythmogenic substrate | en_US |
dc.type | Article | en_US |
dc.rights.holder | © 2022 by the authors. Published by Springer Nature | |
dc.identifier.doi | 10.1007/s11517-022-02621-0 | |
pubs.author-url | https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000828915500002&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=612ae0d773dcbdba3046f6df545e9f6a | en_US |
pubs.issue | 9 | en_US |
pubs.notes | Not known | en_US |
pubs.publication-status | Published | en_US |
pubs.volume | 60 | 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 |