Title :
Stimulus Protocol Determines the Most Computationally Efficient Preconditioner for the Bidomain Equations
Author :
Bernabeu, Miguel O. ; Pathmanathan, Pras ; Pitt-Francis, Joe ; Kay, David
Author_Institution :
Oxford Univ. Comput. Lab., Oxford, UK
Abstract :
The efficient solution of the bidomain equations is a fundamental tool in the field of cardiac electrophysiology. When choosing a finite element discretization of the coupled system, one has to deal with the solution of a large, highly sparse system of linear equations. The conjugate gradient algorithm, along with suitable preconditioning, is the natural choice in this scenario. In this study, we identify the optimal preconditioners with respect to both stimulus protocol and mesh geometry. The results are supported by a comprehensive study of the mesh-dependence properties of several preconditioning techniques found in the literature. Our results show that when only intracellular stimulus is considered, incomplete LU factorization remains a valid choice for current cardiac geometries. However, when extracellular shocks are delivered to tissue, preconditioners that take into account the structure of the system minimize execution time and ensure mesh-independent convergence.
Keywords :
biological tissues; cellular biophysics; electrocardiography; finite element analysis; medical computing; mesh generation; algebraic multigrid technique; bidomain equations; biological tissue; cardiac electrophysiology; conjugate gradient algorithm; current cardiac geometry; extracellular shocks; finite element solution; incomplete LU factorization; intracellular stimulus; mesh geometry; mesh-dependence property; mesh-independent convergence; stimulus protocol; Computational efficiency; Convergence; Electric shock; Electrophysiology; Equations; Finite element methods; Mathematical model; Bidomain equations; computational efficiency; preconditioning; whole heart geometries; Algorithms; Anisotropy; Electrophysiologic Techniques, Cardiac; Finite Element Analysis; Heart; Humans; Linear Models; Models, Cardiovascular; Signal Processing, Computer-Assisted;
Journal_Title :
Biomedical Engineering, IEEE Transactions on
DOI :
10.1109/TBME.2010.2078817