A computational study of the effects of remodelled electrophysiology and   mechanics on initiation of ventricular fibrillation in human heart failure
  The study of pathological cardiac conditions such as arrhythmias, a major cause of mortality in heart failure, is becoming increasingly informed by computational simulation, numerically modelling the governing equations. This can provide insight where experimental work is constrained by technical limitations and/or ethical issues.   As the models become more realistic, the construction of efficient and accurate computational models becomes increasingly challenging. In particular, recent developments have started to couple the electrophysiology models with mechanical models in order to investigate the effect of tissue deformation on arrhythmogenesis, thus introducing an element of nonlinearity into the mathematical representation. This paper outlines a biophysically-detailed computational model of coupled electromechanical cardiac activity which uses the finite element method to approximate both electrical and mechanical systems on unstructured, deforming, meshes. An ILU preconditioner is applied to improve performance of the solver.   This software is used to examine the role of electrophysiology, fibrosis and mechanical deformation on the stability of spiral wave dynamics in human ventricular tissue by applying it to models of both healthy and failing tissue. The latter was simulated by modifying (i) cellular electrophysiological properties, to generate an increased action potential duration and altered intracellular calcium handling, and (ii) tissue-level properties, to simulate the gap junction remodelling, fibrosis and increased tissue stiffness seen in heart failure. The resulting numerical experiments suggest that, for the chosen mathematical models of electrophysiology and mechanical response, introducing tissue level fibrosis can have a destabilising effect on the dynamics, while the net effect of the electrophysiological remodelling stabilises the system. 
Nathan Kirk, Alan Benson, Christopher Goodyer, Matthew Hubbard
cs.CE cs.NA math.NA q-bio.TO