Key result
A new technique for simplifying human ventricular cell models reduced a 17-state variable model to two variables, maintaining acceptable behaviors while running 55 times faster than the original.
Why the study?
High computational costs of complex mathematical models make them difficult to apply in large-scale cardiac electrophysiology simulations.
A novel mathematical reduction technique simplifies human ventricular cell models, significantly improving computational speed by 55 times while maintaining acceptable electrophysiological behavior.
May accelerate in silico cardiac research; leaves open clinical translation of reduced models.
Mathematical modelling and computer simulations are important tools in the field of cardiac electrophysiology. High computational costs of complex models make them difficult to apply in large-scale simulations like tissue. Therefore, model reduction are of particular importance in heart studies. In this paper, we introduce a technique for simplification of ventricular cell(VC) complex models. By using this technique, starting with a complex model of human VC including 17state variables, we reduce the number of state variables to two. Our simplified model is compared with the original one via several electrophysiological features and computational efficiency. Results show that the reduced model has acceptable behaviours in single cell and one-dimensional simulation, moreover, is 55 times faster than the original one. As the presented method does not depend on the reference model, it may be applied to every cardiac cell models or each complex excitable dynamical systems with the same dynamics as VC.
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Sabzpoushan et al. (2019) studied Cardiac electrophysiology. Model reduction technique vs. Original 17-state variable complex model was evaluated on Electrophysiological features and computational efficiency. A new technique for simplifying human ventricular cell models reduced a 17-state variable model to two variables, maintaining acceptable behaviors while running 55 times faster than the original.
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