A unique cardiac electrocardiographic 3D model. Toward interpretable AI diagnosis

Rueda, Cristina; Rodríguez Collado, Alejandro; Fernández, Itziar; Canedo, Christian; Ugarte Martínez, María Dolores; Larriba, Yolanda

A unique cardiac electrocardiographic 3D model. Toward interpretable AI diagnosis

dc.contributor.author	Rueda, Cristina
dc.contributor.author	Rodríguez Collado, Alejandro
dc.contributor.author	Fernández, Itziar
dc.contributor.author	Canedo, Christian
dc.contributor.author	Ugarte Martínez, María Dolores
dc.contributor.author	Larriba, Yolanda
dc.contributor.department	Estadística, Informática y Matemáticas	es_ES
dc.contributor.department	Estatistika, Informatika eta Matematika	eu
dc.contributor.department	Institute for Advanced Materials and Mathematics - INAMAT2	en
dc.date.accessioned	2023-04-04T10:44:23Z
dc.date.available	2023-04-04T10:44:23Z
dc.date.issued	2022
dc.date.updated	2023-04-04T10:37:18Z
dc.description.abstract	Mathematical models of cardiac electrical activity are one of the most important tools for elucidating information about heart diagnostics. In this paper, we present an efficient mathematical formulation for this modeling simple enough to be easily parameterized and rich enough to provide realistic signals. It relies on a five dipole representation of the cardiac electric source, each one associated with the well-known waves of the electrocardiogram signal. Beyond the physical basis of the model, the parameters are physiologically interpretable as they characterize the wave shape, similar to what a physician would look for in signals, thus making them very useful in diagnosis. The model accurately reproduces the electrocardiogram signals of any diseased or healthy heart. This new discovery represents a significant advance in electrocardiography research. It is especially useful for diagnosis, patient follow-up or decision-making on new therapies; is also a promising tool for well-performing, transparent and interpretable AI approaches.	en
dc.description.sponsorship	The authors gratefully acknowledge the financial support received by the Spanish Ministry of Science, Innovation and Universities PID2019-106363RB-I00 to C.R., I.F. and Y.L. and by the Eugenio Rodríguez Pascual Foundation Biomedical Research Grants 2021 to I.F., Y.L., A.R-C., C.C. and C.R.	en
dc.format.mimetype	application/pdf	en
dc.identifier.citation	Rueda, C., Rodríguez-Collado, A., Fernández, I., Canedo, C., Ugarte, M. D., & Larriba, Y. (2022). A unique cardiac electrocardiographic 3D model. Toward interpretable AI diagnosis. IScience, 25(12), 105617. https://doi.org/10.1016/j.isci.2022.105617	en
dc.identifier.doi	10.1016/j.isci.2022.105617
dc.identifier.issn	2589-0042
dc.identifier.uri	https://academica-e.unavarra.es/handle/2454/45055
dc.language.iso	eng	en
dc.publisher	Elsevier	en
dc.relation.ispartof	iScience, 2022, 25(12), 1-17	en
dc.relation.projectID	info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-106363RB-I00/ES/
dc.relation.publisherversion	https://doi.org/10.1016/j.isci.2022.105617
dc.rights	© 2022 The Author(s). This is an open access article under the CC BY-NC-ND license.	en
dc.rights.accessRights	info:eu-repo/semantics/openAccess
dc.rights.uri	https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject	Artificial intelligence	en
dc.subject	Cardiovascular medicine	en
dc.subject	Computer-aided diagnosis method	en
dc.title	A unique cardiac electrocardiographic 3D model. Toward interpretable AI diagnosis	en
dc.type	info:eu-repo/semantics/article
dc.type.version	info:eu-repo/semantics/publishedVersion
dspace.entity.type	Publication
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