118938 20240319081009.0 doi 10.3390/app12136438 sideral 130068 ART-2022-130068 eng Albors, C. Meshless electrophysiological modeling of cardiac resynchronization therapy—benchmark analysis with finite-element methods in experimental data 2022 Access copy available to the general public Unrestricted Computational models of cardiac electrophysiology are promising tools for reducing the rates of non-response patients suitable for cardiac resynchronization therapy (CRT) by optimizing electrode placement. The majority of computational models in the literature are mesh-based, primarily using the finite element method (FEM). The generation of patient-specific cardiac meshes has traditionally been a tedious task requiring manual intervention and hindering the modeling of a large number of cases. Meshless models can be a valid alternative due to their mesh quality independence. The organization of challenges such as the CRT-EPiggy19, providing unique experimental data as open access, enables benchmarking analysis of different cardiac computational modeling solutions with quantitative metrics. We present a benchmark analysis of a meshless-based method with finite-element methods for the prediction of cardiac electrical patterns in CRT, based on a subset of the CRT-EPiggy19 dataset. A data assimilation strategy was designed to personalize the most relevant parameters of the electrophysiological simulations and identify the optimal CRT lead configuration. The simulation results obtained with the meshless model were equivalent to FEM, with the most relevant aspect for accurate CRT predictions being the parameter personalization strategy (e.g., regional conduction velocity distribution, including the Purkinje system and CRT lead distribution). © 2022 by the authors. Licensee MDPI, Basel, Switzerland. info:eu-repo/grantAgreement/ES/DGA/LMP94_21 info:eu-repo/grantAgreement/ES/MICINN-FEDER/PID2019-105674RB-I00 info:eu-repo/grantAgreement/ES/MICINN-REDINSCOR-RD06-003-008 info:eu-repo/grantAgreement/ES/MICINN/TIN2011-28067 info:eu-repo/grantAgreement/ES/MINECO/DPI2016-75799-R info:eu-repo/semantics/openAccess by http://creativecommons.org/licenses/by/3.0/es/ 2.7 2022 PHYSICS, APPLIED 78 / 160 = 0.488 2022 Q2 T2 ENGINEERING, MULTIDISCIPLINARY 42 / 90 = 0.467 2022 Q2 T2 CHEMISTRY, MULTIDISCIPLINARY 100 / 178 = 0.562 2022 Q3 T2 MATERIALS SCIENCE, MULTIDISCIPLINARY 208 / 343 = 0.606 2022 Q3 T2 0.492 2022 Fluid Flow and Transfer Processes 2022 Q2 Materials Science (miscellaneous) 2022 Q2 Engineering (miscellaneous) 2022 Q2 Instrumentation 2022 Q2 Process Chemistry and Technology 2022 Q3 Computer Science Applications 2022 Q3 4.5 2022 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Lluch, È. Gomez, J. F. Cedilnik, N. Mountris, K. A. Mansi, T. Khamzin, S. Dokuchaev, A. Solovyova, O. Pueyo, E. Universidad de Zaragoza (orcid)0000-0002-1960-407X Sermesant, M. Sebastian, R. Morales, H. G. Camara, O. 5008 800 Universidad de Zaragoza Dpto. Ingeniería Electrón.Com. Área Teoría Señal y Comunicac. 12, 13 (2022), 6438 [27 pp] Appl. sci. Applied Sciences (Switzerland) 2076-3417 1440573 http://zaguan.unizar.es/record/118938/files/texto_completo.pdf Versión publicada 2873537 http://zaguan.unizar.es/record/118938/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:118938 articulos driver 2024-03-18-14:59:21 ARTICLE