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<dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:invenio="http://invenio-software.org/elements/1.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"><dc:identifier>doi:10.23919/CinC49843.2019.9005863</dc:identifier><dc:language>eng</dc:language><dc:creator>Mountris, K.A.</dc:creator><dc:creator>Sanchez, C.</dc:creator><dc:creator>Pueyo, E.</dc:creator><dc:title>A Novel Paradigm for in Silico Simulation of Cardiac Electrophysiology Through the Mixed Collocation Meshless Petrov-Galerkin Method</dc:title><dc:identifier>ART-2019-121995</dc:identifier><dc:description>Multi-scale cardiac electrophysiological modeling involves high computational load due to the inherent complexity as well as to limitations of the employed numerical methods (e.g., Finite Element Method - FEM). This study investigates the use of the Meshless Local Petrov-Galerkin Mixed Collocation (MLPG-MC) method to simulate cardiac electrophysiology. MLPG-MC is a truly meshless method where both the unknown function and its gradient are interpolated using nodal collocation. A 3 cm × 3 cm human ventricular tissue was simulated based on the monodomain reaction-diffusion model using the operator splitting technique. MLPG-MC or FEM were used to solve the diffusion term and the O''Hara-Virág-Varró-Rudy AP model to represent cellular electrophysiology at baseline and under 30% IKr inhibition (IKr30). Mean differences between MLPG-MC and FEM in AP duration at 90% (APD90), 50% (APD50) and 20% (APD20) repolarization levels were 4.47%, 4.16% and 3.29% for baseline conditions and 3.66%, 2.10% and 1.62% for IKr30 conditions. The computational time associated with each of the two methods was comparable. In conclusion, considering that MLPG-MC does not involve any mesh requirements and is well suited for massive parallelization, this study shows that it represents a promising alternative to FEM for cardiac electrophysiology simulations.</dc:description><dc:date>2019</dc:date><dc:source>http://zaguan.unizar.es/record/98277</dc:source><dc:doi>10.23919/CinC49843.2019.9005863</dc:doi><dc:identifier>http://zaguan.unizar.es/record/98277</dc:identifier><dc:identifier>oai:zaguan.unizar.es:98277</dc:identifier><dc:relation>info:eu-repo/grantAgreement/ES/DGA-FEDER/T39-17R-BSICoS</dc:relation><dc:relation>info:eu-repo/grantAgreement/EUR/ERC-2014-StG-638284</dc:relation><dc:relation>info:eu-repo/grantAgreement/ES/MINECO/DPI2016-75458-R</dc:relation><dc:identifier.citation>Computing in Cardiology 46 (2019), [4 pp]</dc:identifier.citation><dc:rights>by</dc:rights><dc:rights>http://creativecommons.org/licenses/by/3.0/es/</dc:rights><dc:rights>info:eu-repo/semantics/openAccess</dc:rights></dc:dc>

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