Current mathematical models of the cardiovascular system that are based on systems of ordinary differential equations are limited in their ability to mimic important features of measured patient data, such as variable heart rates (HR). Such limitations present a significant obstacle in the use of such models for clinical decision-making, as it is the variations in vital signs such as HR and systolic and diastolic blood pressure that are monitored and recorded in typical critical care bedside monitoring systems. In this paper, novel extensions to well-established multi-compartmental models of the cardiovascular and respiratory systems are proposed that permit the simulation of variable HR. Furthermore, a correction to current models is also proposed to stabilize the respiratory behaviour and enable realistic simulation of vital signs over the longer time scales required for clinical management. The results of the extended model developed here show better agreement with measured bio-signals, and these extensions provide an important first step towards estimating model parameters from patient data, using methods such as neural ordinary differential equations. The approach presented is generalizable to many other similar multi-compartmental models of the cardiovascular and respiratory systems.

中文翻译：

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心血管和呼吸系统的可变心率多室耦合模型。

当前基于常微分方程组的心血管系统数学模型在模拟测量的患者数据的重要特征（例如可变心率（HR））方面的能力有限。这些限制对使用此类模型进行临床决策构成了重大障碍，因为在典型的重症监护床边监测系统中监测和记录的是心率、收缩压和舒张压等生命体征的变化。在本文中，提出了对心血管和呼吸系统的成熟多室模型的新扩展，允许模拟可变心率。此外，还建议对当前模型进行修正，以稳定呼吸行为，并能够在临床管理所需的较长时间范围内真实模拟生命体征。这里开发的扩展模型的结果显示出与测量的生物信号更好的一致性，并且这些扩展为使用神经常微分方程等方法从患者数据估计模型参数提供了重要的第一步。所提出的方法可推广到许多其他类似的心血管和呼吸系统多室模型。