Abstract
We present a new phenomenological model of human ventricular epicardial cells and we test its reentry dynamics. The model is derived from the Rogers-McCulloch formulation of the FitzHugh-Nagumo equations and represents the total ionic current divided into three contributions corresponding to the excitatory, recovery and transient outward currents. Our model reproduces the main characteristics of human epicardial tissue, including action potential amplitude and morphology, upstroke velocity, and action potential duration and conduction velocity restitution curves. The reentry dynamics is stable, and the dominant period is about 270 ms, which is comparable to clinical values. The proposed model is the first phenomenological model able to accurately resemble human experimental data by using only 3 state variables and 17 parameters. Indeed, it is more computationally efficient than existing models (i.e., almost two times faster than the minimal ventricular model). Beyond the computational efficiency, the low number of parameters facilitates the process of fitting the model to the experimental data.
Highlights
Heart disease is a leading cause of death worldwide and causes millions of victims every year
We found that 6 beats are enough to achieve steady-state action potential duration (APD) and conduction velocity (CV)
We have presented a novel model for human epicardial tissue derived from the RogersMcCulloch formulation of the FHN model
Summary
Heart disease is a leading cause of death worldwide and causes millions of victims every year. The leading cause of mortality is ischaemic heart disease which accounts for 41% of all CVD deaths. Mathematical models of the electrical activity of the heart are recognized as important tools in modern cardiac research. They are extremely useful in understanding of cardiac pathophysiology, for example, in case of cardiac arrhythmias. Mathematical models could be employed to simulate clinical recordings (e.g., ECG) under both healthy and pathological conditions. Simulations of such signals are very helpful for the development of new diagnostic tools. The possibilities for doing experimental and clinical studies involving human heart tissues are extremely limited. Modelling heart tissue electrical activity requires the use of ionic models
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