A bond graph model of the cardiovascular system.

Area of Science:

• Physiology
• Biomedical Engineering
• Computational Modeling

Background:

• Autonomic nervous system (ANS) function provides key indicators for cardiovascular disease risk and early detection.
• Analyzing ANS data is challenging due to the complex autonomic regulation of the cardiovascular system (CVS).
• Existing models for ANS-CVS interaction are limited in scope.

Purpose of the Study:

• To present a novel, integrated model of the cardiovascular system (CVS) and its regulation by the autonomic nervous system (ANS).
• To facilitate the analysis of complex cardiovascular data from autonomic function tests.

Main Methods:

• Developed models for vascular system and ventricular activity using Bond Graph formalism for integrated energetic representation.
• Integrated an electrophysiologic model of cardiac action potential using ordinary differential equations.
• Represented short-term ANS regulation (heart rate, contractility, vasoconstriction) using continuous transfer functions.
• Coupled multi-formalism models using a simulation library.

Main Results:

• Successfully simulated cardiovascular responses during Tilt Test and Valsalva Manoeuvre.
• Validated the model by comparing simulated signals with real data from autonomic tests.
• Demonstrated the model's capability to represent electro-mechanical and regulatory aspects of the CVS.

Conclusions:

• The proposed multi-formalism model effectively captures the complex interactions within the cardiovascular system and its autonomic regulation.
• This integrated modeling approach offers a promising tool for analyzing autonomic function test data.
• The model can enhance risk stratification and early detection of cardiovascular pathologies.