Fast Characterization of Inducible Regions of Atrial Fibrillation Models With Multi-Fidelity Gaussian Process Classification

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dc.article.number757159
dc.catalogadorgjm
dc.contributor.authorGander, Lia
dc.contributor.authorPezzuto, Simone
dc.contributor.authorGharaviri, Ali
dc.contributor.authorKrause, Rolf
dc.contributor.authorPerdikaris, Paris
dc.contributor.authorSahli Costabal, Francisco
dc.date.accessioned2024-05-30T16:23:24Z
dc.date.available2024-05-30T16:23:24Z
dc.date.issued2022
dc.description.abstractComputational models of atrial fibrillation have successfully been used to predict optimal ablation sites. A critical step to assess the effect of an ablation pattern is to pace the model from different, potentially random, locations to determine whether arrhythmias can be induced in the atria. In this work, we propose to use multi-fidelity Gaussian process classification on Riemannian manifolds to efficiently determine the regions in the atria where arrhythmias are inducible. We build a probabilistic classifier that operates directly on the atrial surface. We take advantage of lower resolution models to explore the atrial surface and combine seamlessly with high-resolution models to identify regions of inducibility. We test our methodology in 9 different cases, with different levels of fibrosis and ablation treatments, totalling 1,800 high resolution and 900 low resolution simulations of atrial fibrillation. When trained with 40 samples, our multi-fidelity classifier that combines low and high resolution models, shows a balanced accuracy that is, on average, 5.7% higher than a nearest neighbor classifier. We hope that this new technique will allow faster and more precise clinical applications of computational models for atrial fibrillation. All data and code accompanying this manuscript will be made publicly available at: https://github.com/fsahli/AtrialMFclass.
dc.fechaingreso.objetodigital2024-09-25
dc.format.extent16 páginas
dc.fuente.origenORCID
dc.identifier.doi10.3389/fphys.2022.757159
dc.identifier.urihttps://doi.org/10.3389/fphys.2022.757159
dc.identifier.urihttp://www.scopus.com/inward/record.url?eid=2-s2.0-85127231970&partnerID=MN8TOARS
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/86075
dc.identifier.wosidWOS:000782760400001
dc.information.autorucEscuela de Ingeniería; Sahli Costabal, Francisco; S/I; 154857
dc.language.isoen
dc.nota.accesocontenido completo
dc.revistaFrontiers in Physiology
dc.rightsacceso abierto
dc.rights.licenseCC BY 4.0 DEED Attribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectMachine learning
dc.subjectCardiac electrophysiology
dc.subjectAtrial fibrillation
dc.subjectGaussian processes
dc.subjectRiemannian manifolds
dc.subjectActive learning
dc.subject.ddc600
dc.subject.deweyTecnologíaes_ES
dc.subject.ods03 Good health and well-being
dc.subject.odspa03 Salud y bienestar
dc.titleFast Characterization of Inducible Regions of Atrial Fibrillation Models With Multi-Fidelity Gaussian Process Classification
dc.typeartículo
dc.volumen13
sipa.codpersvinculados154857
sipa.trazabilidadORCID;2024-05-27
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