ArticleIEEE transactions on bio-medical engineering2023
Nonlinear Closed-Loop Predictive Control of Heart Rate and Blood Pressure Using Vagus Nerve Stimulation: An In Silico Study.
Article in IEEE transactions on bio-medical engineering, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- A compartmental model for simulating the gut-brain axis in gastric function regulation.Frontiers in physiology · 2026Article
- A Compartmental Model for Simulating the Gut-Brain Axis in Gastric Function Regulation.bioRxiv : the preprint server for biology · 2025Article
- Multiple Model Predictive Control of the Cardiovascular System using Vagal Nerve Stimulation.IEEE transactions on control systems technology : a publication of the IEEE Control Systems Society · 2025Article
- Computational modeling of autonomic nerve stimulation: Vagus et al.Current opinion in biomedical engineering · 2024Article
- Reinforcement learning for closed-loop regulation of cardiovascular system with vagus nerve stimulation: a computational study.Journal of neural engineering · 2024Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
We propose a nonlinear model-based control technique for regulating the heart rate and blood pressure using vagus nerve neuromodulation. The closed-loop framework is based on an in silico model of the rat cardiovascular system for the simulation of the hemodynamic response to multi-location vagal nerve stimulation. The in silico model is derived by compartmentalizing the various physiological components involved in the closed-loop cardiovascular system with intrinsic baroreflex regulation to virtually generate nominal and hypertension-related heart dynamics of rats in rest and exercise states. The controller, using a reduced cycle-averaged model, monitors the outputs from the in silico model, estimates the current state of the reduced model, and computes the optimum stimulation locations and the corresponding parameters using a nonlinear model predictive control algorithm. The results demonstrate that the proposed control strategy is robust with respect to its ability to handle setpoint tracking and disturbance rejection in different simulation scenarios.
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Registered trials
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