Computational framework for simulating the mechanisms and ECG of re-entrant ventricular fibrillation

Abstract

Ventricular arrhythmias remain an important cause of morbidity and mortality in the Western world. Although the underlying mechanisms of these arrhythmias can be studied experimentally, these investigations are in general limited to mapping electrical activity on the heart surface. Computational models of action potential propagation offer a potentially powerful way to study electrical activation and arrhythmias, but current models are not easy to link to the clinical environment. In this paper, we describe a framework for computing action potential propagation in which the geometry, electrophysiology and regional properties of ventricular myocardium can be specified so that, for example, different models for cardiac cellular electrophysiology can be used. We have computed action potential propagation during both normal beats and re-entry in an anatomically accurate model of ventricular geometry. By computing the resultant electric current flow in the torso we have also generated simulated ECG signals that result from specific activation patterns in the ventricular model. Models can be powerful tools for explaining observations, and this approach is able to provide a direct link between the different configurations of re-entry observed in computational and experimental studies, and the ECG signals observed in patients.

Filament Behavior in a Computational Model of Ventricular Fibrillation in the Canine Heart

“…Despite these simplifications, the APD and CV for action potential propagation in our model were within the range of values reported in experimental studies, the activation sequence of our simulated ventricles also matched with experimental observations [31], and the CL of re-entry during simulated VF was within the range of values reported experimentally [8].…”

“…We sampled the finite element description of both shape and fiber orientation on a regular Cartesian grid to give a ventricular geometry with 5 567 778 grid points [31], [32]. We have shown in a previous paper that the activation sequence following initiation of a normal beat by stimulating the endocardial surface was complete in 50 ms, and was quantitatively comparable with measured activation in canine ventricles [31].…”

Model of Bipolar Electrogram Fractionation and Conduction Block Associated With Activation Wavefront Direction at Infarct Border Zone Lateral Isthmus Boundaries

“…where θ 0 is the conduction velocity when there is no curvature along the leading edge of the activation wavefront (ie, when the wavefront is rectilinear), D is the diffusion coefficient, which is the current flow because of the cell membrane voltage gradient (0.05−0.2 mm 2 /ms in ventricular myocardium), 18 c is the spatial resolution, or space step, in units of millimeters, T is IBZ thickness, where the thickness direction, perpendicular to the epicardial surface, is defined to be the Z axis, and ΔT is the IBZ thickness change over 1 space step as the leading edge of the wavefront propagates in the XY plane. When the thickness transition is thin-to-thick, the wavefront leading edge becomes convex, that is, it is curved outward so that the conducting volume being activated is greater than the volume previously activated, and the sign in Equation 1 is negative, resulting in wavefront slowing.…”

Integrated Heart—Coupling multiscale and multiphysics models for the simulation of the cardiac function

“…In this regard we first list some of the straightforward extensions to the model compartments we have been already studied in previous contributions [185,216,219]. Some specific aspects of modelling cardiac function that have not been covered include the fast conduction system (Purkinje network [264] and Purkinje-muscle junctions), cardiac perfusion [46] and its coupling with coronary flow [66,169], autoregulation aspects of heart rate [139], myocardial tissue damage and remodelling [101], the modelling of the atria [47,64,252], the heart-torso coupling that is needed for the simulation of an ECG [33,55,75], fluid dynamics in idealized ventricles [194,248,249], and many others. As discussed in Sect.…”