Abstract
The heart consists of a complex network of billions of cells. Under physiological conditions, cardiac cells propagate electrical signals in space, generating the heartbeat in a synchronous and coordinated manner. When such a synchronization fails, life-threatening events can arise. The inherent complexity of the underlying nonlinear dynamics and the large number of biological components involved make the modeling and the analysis of electrophysiological properties in cardiac tissue still an open challenge. We consider here a Hybrid Cellular Automata (HCA) approach modeling the cardiac cell-cell membrane resistance with a free variable. We show that the modeling approach can reproduce important and complex spatiotemporal properties paving the ground for promising future applications. We show how GPU-based technology can considerably accelerate the simulation and the analysis. Furthermore, we study the cardiac behavior within a unidimensional domain considering inhomogeneous resistance and we perform a Monte Carlo analysis to evaluate our approach.
Highlights
Computational cardiology provides a safe, non-invasive, and ethical method to investigate the heart and its dysfunctionalities
We developed two kernels: the first calculates the diffusion of the action potential among the cells using the values of the first neighbors for each cell; the second computes the numerical integration for a time-step of the piece-wise nonlinear ordinary differential equations modeling the inward/outward current flows in each cardiac cell
We have presented an Hybrid Cellular Automata (HCA) modeling the cardiac cell-cell membrane resistance with a free variable
Summary
Computational cardiology provides a safe, non-invasive, and ethical method to investigate the heart and its dysfunctionalities. Mathematical models [1,2,3,4] extend the pioneering work of Hodgkin-Huxley [5] to describe and explain, with a set of ordinary differential equations (ODEs), the ionic mechanisms responsible for the initiation and the electrical propagation of action potentials traversing excitable cells such as cardiac myocytes and neurons [6,7]. These early studies enabled innovative in-silico research and clinically oriented applications [8]. A cellular automaton consists of a regular grid of cells where a discrete state is associated to each cell
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