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Prediction of CRT Activation Sequence by Personalization of Biventricular Models from Electroanatomical Maps

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Prediction of CRT Activation Sequence by Personalization of Biventricular Models from Electroanatomical Maps

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dc.contributor.author Gómez García, Juan Francisco es_ES
dc.contributor.author Trenor Gomis, Beatriz Ana es_ES
dc.contributor.author Sebastián Aguilar, Rafael es_ES
dc.date.accessioned 2022-01-28T07:40:52Z
dc.date.available 2022-01-28T07:40:52Z
dc.date.issued 2020-01-23 es_ES
dc.identifier.issn 0302-9743 es_ES
dc.identifier.uri http://hdl.handle.net/10251/180319
dc.description.abstract [EN] Optimization of lead placement and interventricular delay settings in patients under cardiac resynchronization therapy is a complex task that might benefit from prior information based on models. Biophysical models can be used to predict the sequence of electrical heart activation in a patient given a set of parameters which should be personalized to the patient. In this paper, we use electroanatomical maps to personalize the endocardial activation of the right ventricle, and the different tissue conductivities in a pig model with left bundle branch block, to reproduce personalized biventricular activations. Following, we tested the personalized heart model by virtually simulating cardiac resynchronization therapy es_ES
dc.description.sponsorship This work was supported by the "Plan Estatal de Investigacion Cientifica y Tecnica y de Innovacion 2013-2016" from the Ministerio de Economia, Industria y Competitividad of Spain and Fondo Europeo de Desarrollo Regional (FEDER) DPI2016-75799-R (AEI/FEDER, UE), and by the Direccion General de Politica Cientifica de la Generalitat Valenciana (PROMETEU 2016/088) es_ES
dc.language Inglés es_ES
dc.publisher Springer-Verlag es_ES
dc.relation.ispartof Lecture Notes in Computer Science es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Cardiac resynchronization therapy es_ES
dc.subject Tissue properties personalization es_ES
dc.subject Biophysical modeling es_ES
dc.subject.classification TECNOLOGIA ELECTRONICA es_ES
dc.title Prediction of CRT Activation Sequence by Personalization of Biventricular Models from Electroanatomical Maps es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1007/978-3-030-39074-7_36 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/GENERALITAT VALENCIANA//PROMETEO%2F2016%2F088//MODELOS COMPUTACIONALES PERSONALIZADOS MULTI-ESCALA PARA LA OPTIMIZACION DEL DIAGNOSTICO Y TRATAMIENTO DE ARRITMIAS CARDIACAS (PERSONALISED DIGITAL HEART)/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI//DPI2016-75799-R//TECNOLOGIAS COMPUTACIONALES PARA LA OPTIMIZACION DE TERAPIAS PERSONALIZADAS DE PATOLOGIAS AURICULARES Y VENTRICULARES/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería Electrónica - Departament d'Enginyeria Electrònica es_ES
dc.contributor.affiliation Universitat Politècnica de València. Instituto Interuniversitario de Investigación en Bioingeniería y Tecnología Orientada al Ser Humano - Institut Interuniversitari d'Investigació en Bioenginyeria i Tecnologia Orientada a l'Ésser Humà es_ES
dc.description.bibliographicCitation Gómez García, JF.; Trenor Gomis, BA.; Sebastián Aguilar, R. (2020). Prediction of CRT Activation Sequence by Personalization of Biventricular Models from Electroanatomical Maps. Lecture Notes in Computer Science. 12009:342-351. https://doi.org/10.1007/978-3-030-39074-7_36 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1007/978-3-030-39074-7_36 es_ES
dc.description.upvformatpinicio 342 es_ES
dc.description.upvformatpfin 351 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 12009 es_ES
dc.relation.pasarela S\443428 es_ES
dc.contributor.funder GENERALITAT VALENCIANA es_ES
dc.contributor.funder AGENCIA ESTATAL DE INVESTIGACION es_ES
dc.contributor.funder European Regional Development Fund es_ES
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dc.subject.ods 03.- Garantizar una vida saludable y promover el bienestar para todos y todas en todas las edades es_ES


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