Key result
The particle swarm optimization (PSO) method performed better than the non-linear square fit (NLSF) approach in computing parameters for the Windkessel arterial flow model based on P-RMS calculations.
Why the study?
Computational models of arterial blood flow are complex with unknown parameters that require optimization using in-vivo data for physiological relevance, prompting the need for an efficient and accurate optimization algorithm.
The particle swarm optimization method provides an accurate and superior alternative to classical gradient-based algorithms for identifying physiological parameters in arterial blood flow models.
Answers are grounded in this paper and cite their sources.
May refine arterial model parameters in research settings; leaves open clinical translation pending validation.
• Computational modeling and simulations of the arterial blood flow network offer insilico platform for both diagnostics and therapeutic phases. • For physiological relevance, model parameters should be optimized using in-vivo human/animal data sets. • The particle swarm optimization (PSO) method offers an efficient, yet an accurate optimization algorithm to compute parameters in the arterial blood flow models. • Model parameters obtained using the proposed PSO method was able to recover the aortic pressure distribution during the cardiac cycle in both normal and diseased conditions. Background and objective: Mathematical modeling and computational simulations of arterial blood flow network can offer an insilico platform for both diagnostics and therapeutic phases of patients that suffer from cardiac diseases. These models are normally complex and involve many unknown parameters. For physiological relevance, these parameters should be optimized using in-vivo human/animal data sets. The main goal of this work is to develop an efficient, yet an accurate optimization algorithm to compute parameters in the arterial blood flow models. Methods: The particle swarm optimization (PSO) method is proposed herein for the first time, as an accurate algorithm that applies to computing parameters in the Windkessel type model of blood flow in the arterial system. We begin by defining a 6-element Windkessel (WK6) arterial flow model, which is then implemented and validated using multiple flow rate and aortic pressure measurements obtained from different subjects including dogs, pigs and humans. The parameters in the model are obtained using the PSO technique which minimizes the pressure root mean square (P-RMS) error between the computed and the measured aortic pressure waveform. Results: Model parameters obtained using the proposed PSO method were able to recover the pressure waveform in the aorta during the cardiac cycle for both healthy and diseased species (animals/humans). The PSO method provides an accurate approach to solve this challenging multi-dimensional parameter identification problem. The results obtained by PSO algorithm was compared with the classical gradient-based, namely the non-linear square fit (NLSF) algorithm. Conclusions: The results indicate that the PSO method offers alternative and accurate method to find optimal physiological parameters involved in the Windkessel model for the study of arterial blood flow network. The PSO method has performed better than the NLSF approach as depicted from the P-RMS calculations. Finally, we believe that the PSO method offers a great potential and could be used for many other biomedicine optimization problems.
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Aboelkassem et al. (2021) studied Cardiac diseases. Particle swarm optimization (PSO) method vs. Non-linear square fit (NLSF) algorithm was evaluated on Pressure root mean square (P-RMS) error between computed and measured aortic pressure waveform. The particle swarm optimization (PSO) method performed better than the non-linear square fit (NLSF) approach in computing parameters for the Windkessel arterial flow model based on P-RMS calculations.
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