Inertance estimation in a lumped-parameter hydraulic simulator of human circulation

Area of Science:

• Biomedical Engineering
• Cardiovascular Physiology
• In Vitro Modeling

Background:

• Pulsatile mock loop systems are crucial for in vitro cardiovascular research, simulating heart and vasculature.
• Accurate component dimensioning is vital for reliable physiopathological mimicry.
• Afterload inertance is challenging to quantify directly and often neglected or qualitatively assessed in existing systems.

Purpose of the Study:

• To develop and validate quantitative methods for estimating afterload inertance in mock loop systems.
• To compare the efficacy of Maximum-likelihood estimation (MLE) and Bayesian estimation for inertance quantification.
• To assess the inertance of a real mock loop system and its physiological relevance.

Main Methods:

• Proposed two novel quantitative methods: Maximum-likelihood estimation (MLE) and Bayesian estimation.
• Applied these methods to analyze pressure and flow waveforms from a pulsatile mock loop.
• Validated the methods on a real-world mock loop experimental setup.

Main Results:

• The studied mock loop system exhibited an inertance comparable to the systemic circulation's reference value.
• Estimated inertance showed expected variations with changes in average flow and pulse frequency.
• Bayesian estimation yielded higher and more consistent inertance values than MLE.

Conclusions:

• The proposed MLE and Bayesian methods offer reliable quantitative assessment of afterload inertance.
• The Bayesian approach demonstrates superior performance for inertance estimation in mock loop systems.
• Accurate inertance quantification enhances the fidelity of in vitro cardiovascular simulations.