Publications by authors named "Gevik Grigorian"
Article Synopsis
- The interaction between the left and right ventricles in the cardiovascular system is complicated and influenced by structures like the septum and pericardium, leading to complex nonlinear equations in modeling.
- Existing computational models often oversimplify these interactions or ignore them altogether, prompting a search for better methods.
- The proposed approach utilizes a hybrid neural ordinary differential equation model that incorporates neural networks to simulate ventricular interactions, demonstrating strong predictive abilities even with added noise in the data.
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.
View Article and Find Full Text PDF