Cardiovascular diseases are among the leading causes of death worldwide. In this context, the use of virtual cardiac models has emerged as a promising approach to study the mechanisms underlying these conditions in a minimally invasive manner, while also supporting diagnosis and treatment. A widely used strategy to build such models relies on partial differential equations (PDEs) and ordinary differential equations (ODEs). Although these biophysical models can provide accurate results, their computational cost grows significantly with domain size. As an alternative, phenomenological models such as cellular automata (CA) offer a more efficient solution, provided that their states and transition rules are carefully designed to capture the relevant physiological behavior.
In this study, we present a cellular automaton based on events, capable of efficiently simulating electrical propagation in cardiac tissue. To this end, we use the monodomain model both to describe the biophysical propagation of electrical signals and to generate calibration data for the automaton. The activation time and action potential duration recorded for each cell are used to adjust propagation velocity in the automaton across different pacing cycle lengths and fiber orientations. Anisotropy is modeled by locally solving the Eikonal equation.
Finally, we perform simulations under varying fiber orientations and stimulation scenarios and compare the automaton's results with those obtained from the monodomain model. The comparison shows good agreement, demonstrating the accuracy and potential of the proposed cellular automaton as a fast and reliable tool for cardiac electrophysiology studies.