A method for reduction of human ventricular action potential model

IF 1.8 4区数学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Mathematical and Computer Modelling of Dynamical Systems Pub Date : 2020-01-02 DOI:10.1080/13873954.2019.1701039

S. Sabzpoushan, A. Ghajarjazy

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引用次数: 1

Abstract

ABSTRACT 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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一种简化人心室动作电位模型的方法

数学建模和计算机模拟是心脏电生理领域的重要工具。复杂模型的高计算成本使其难以应用于组织等大规模模拟。因此，模型还原在心脏研究中尤为重要。本文介绍了一种简化心室细胞(VC)复杂模型的技术。通过使用这种技术，从包含17个状态变量的人类VC复杂模型开始，我们将状态变量的数量减少到2个。从电生理特征和计算效率两方面对简化模型进行了比较。结果表明，简化后的模型在单细胞和一维模拟中均具有良好的性能，并且速度比原模型快55倍。由于该方法不依赖于参考模型，因此可以应用于所有心脏细胞模型或具有相同动力学特性的复杂可激动力系统。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Mathematical and Computer Modelling of Dynamical Systems 数学-计算机：跨学科应用

CiteScore

3.80

自引率

5.30%

发文量

审稿时长

>12 weeks

期刊介绍： Mathematical and Computer Modelling of Dynamical Systems (MCMDS) publishes high quality international research that presents new ideas and approaches in the derivation, simplification, and validation of models and sub-models of relevance to complex (real-world) dynamical systems. The journal brings together engineers and scientists working in different areas of application and/or theory where researchers can learn about recent developments across engineering, environmental systems, and biotechnology amongst other fields. As MCMDS covers a wide range of application areas, papers aim to be accessible to readers who are not necessarily experts in the specific area of application. MCMDS welcomes original articles on a range of topics including: -methods of modelling and simulation- automation of modelling- qualitative and modular modelling- data-based and learning-based modelling- uncertainties and the effects of modelling errors on system performance- application of modelling to complex real-world systems.