Literature DB >> 16773461

On the computational complexity of the bidomain and the monodomain models of electrophysiology.

Joakim Sundnes1, Bjørn Fredrik Nielsen, Kent Andre Mardal, Xing Cai, Glenn Terje Lines, Aslak Tveito.   

Abstract

The bidomain model, coupled with accurate models of cell membrane kinetics, is generally believed to provide a reasonable basis for numerical simulations of cardiac electrophysiology. Because of changes occurring in very short time intervals and over small spatial domains, discretized versions of these models must be solved on fine computational grids, and small time-steps must be applied. This leads to huge computational challenges that have been addressed by several authors. One popular way of reducing the CPU demands is to approximate the bidomain model by the monodomain model, and thus reducing a two by two set of partial differential equations to one scalar partial differential equation; both of which are coupled to a set of ordinary differential equations modeling the cell membrane kinetics. A reduction in CPU time of two orders of magnitude has been reported. It is the purpose of the present paper to provide arguments that such a reduction is not present when order-optimal numerical methods are applied. Theoretical considerations and numerical experiments indicate that the reduction factor of the CPU requirements from bidomain to monodomain computations, using order-optimal methods, typically is about 10 for simple cell models and less than two for more complex cell models.

Entities:  

Mesh:

Year:  2006        PMID: 16773461     DOI: 10.1007/s10439-006-9082-z

Source DB:  PubMed          Journal:  Ann Biomed Eng        ISSN: 0090-6964            Impact factor:   3.934


  13 in total

1.  Verification of cardiac tissue electrophysiology simulators using an N-version benchmark.

Authors:  Steven A Niederer; Eric Kerfoot; Alan P Benson; Miguel O Bernabeu; Olivier Bernus; Chris Bradley; Elizabeth M Cherry; Richard Clayton; Flavio H Fenton; Alan Garny; Elvio Heidenreich; Sander Land; Mary Maleckar; Pras Pathmanathan; Gernot Plank; José F Rodríguez; Ishani Roy; Frank B Sachse; Gunnar Seemann; Ola Skavhaug; Nic P Smith
Journal:  Philos Trans A Math Phys Eng Sci       Date:  2011-11-13       Impact factor: 4.226

Review 2.  Modeling defibrillation of the heart: approaches and insights.

Authors:  Natalia Trayanova; Jason Constantino; Takashi Ashihara; Gernot Plank
Journal:  IEEE Rev Biomed Eng       Date:  2011

3.  Predicting electromyographic signals under realistic conditions using a multiscale chemo-electro-mechanical finite element model.

Authors:  Mylena Mordhorst; Thomas Heidlauf; Oliver Röhrle
Journal:  Interface Focus       Date:  2015-04-06       Impact factor: 3.906

4.  A macro finite-element formulation for cardiac electrophysiology simulations using hybrid unstructured grids.

Authors:  Bernardo M Rocha; Ferdinand Kickinger; Anton J Prassl; Gundolf Haase; Edward J Vigmond; Rodrigo Weber dos Santos; Sabine Zaglmayr; Gernot Plank
Journal:  IEEE Trans Biomed Eng       Date:  2010-08-09       Impact factor: 4.538

5.  Solving the coupled system improves computational efficiency of the bidomain equations.

Authors:  James A Southern; Gernot Plank; Edward J Vigmond; Jonathan P Whiteley
Journal:  IEEE Trans Biomed Eng       Date:  2009-05-19       Impact factor: 4.538

6.  An electrodiffusive neuron-extracellular-glia model for exploring the genesis of slow potentials in the brain.

Authors:  Marte J Sætra; Gaute T Einevoll; Geir Halnes
Journal:  PLoS Comput Biol       Date:  2021-07-16       Impact factor: 4.475

7.  Simple model for identifying critical regions in atrial fibrillation.

Authors:  Kim Christensen; Kishan A Manani; Nicholas S Peters
Journal:  Phys Rev Lett       Date:  2015-01-16       Impact factor: 9.161

8.  Parallel Optimization of 3D Cardiac Electrophysiological Model Using GPU.

Authors:  Yong Xia; Kuanquan Wang; Henggui Zhang
Journal:  Comput Math Methods Med       Date:  2015-10-25       Impact factor: 2.238

9.  Modeling the chemoelectromechanical behavior of skeletal muscle using the parallel open-source software library OpenCMISS.

Authors:  Thomas Heidlauf; Oliver Röhrle
Journal:  Comput Math Methods Med       Date:  2013-11-17       Impact factor: 2.238

10.  In-silico study of the cardiac arrhythmogenic potential of biomaterial injection therapy.

Authors:  William A Ramírez; Alessio Gizzi; Kevin L Sack; Julius M Guccione; Daniel E Hurtado
Journal:  Sci Rep       Date:  2020-07-31       Impact factor: 4.379

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.