Key result
Monte Carlo-based Windkessel modeling reliably estimates arterial parameters with low computational burden in canine models.
The proposed Monte Carlo method offers a fast and computationally efficient way to estimate arterial parameters from pressure curves.
Should not change clinical practice; leaves open validation of Monte Carlo Windkessel estimation beyond canine models.
Numerous parameter estimation techniques exist for characterizing the arterial system using electrical circuit analogs. However, they are often limited by their requirements and usually high computational burdain. Therefore, a new method for estimating arterial parameters based on Monte Carlo simulation is proposed. A three element Windkessel model was used to represent the arterial system. The approach was to reduce the error between the calculated and physiological aortic pressure by randomly generating arterial parameter values, while keeping constant the arterial resistance. This last value was obtained for each subject using the arterial flow, and was a necessary consideration in order to obtain a unique set of values for the arterial compliance and peripheral resistance. The estimation technique was applied to in vivo data containing steady beats in mongrel dogs, and it reliably estimated Windkessel arterial parameters. Further, this method appears to be computationally efficient for on-line time-domain estimation of these parameters.
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Gostuski et al. (2016) studied this question. Monte Carlo method for Windkessel model parameter estimation was evaluated on Estimation error of Windkessel arterial parameters. A Monte Carlo simulation based on the Windkessel model reliably estimated arterial parameters with low computational burden in mongrel dogs.
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