In this study, the authors present robust tuning rules for an event-based control architecture for the automatic regulation of the depth of hypnosis in anaesthesia. The authors' control system uses propofol and remifentanil coadministration as control variables and the bispectral index as controlled variable. The control system is based on a PIDPlus controller combined with an event generator that detects significant variations of the BIS signal, thus providing strong filtering of the noise. A fixed ratio between the drug infusions allows the anaesthesiologist to explicitly regulate the opioid-hypnotic balance of the anaesthesia. The tuning rules are developed by solving a min-max optimisation problem that optimises the worst-case scenario over a given data set of patient models. A gain scheduling strategy yields optimal performance in both the induction and the maintenance phases of anaesthesia. Finally, through the Monte Carlo method, they validate the effectiveness of the proposed approach on a general population, and the robustness to the intra- and inter-patient variability for different infusion balances.

Event-based control tuning of propofol and remifentanil coadministration for general anaesthesia

Padula F.;Latronico N.;Paltenghi M.;Visioli A.
2020-01-01

Abstract

In this study, the authors present robust tuning rules for an event-based control architecture for the automatic regulation of the depth of hypnosis in anaesthesia. The authors' control system uses propofol and remifentanil coadministration as control variables and the bispectral index as controlled variable. The control system is based on a PIDPlus controller combined with an event generator that detects significant variations of the BIS signal, thus providing strong filtering of the noise. A fixed ratio between the drug infusions allows the anaesthesiologist to explicitly regulate the opioid-hypnotic balance of the anaesthesia. The tuning rules are developed by solving a min-max optimisation problem that optimises the worst-case scenario over a given data set of patient models. A gain scheduling strategy yields optimal performance in both the induction and the maintenance phases of anaesthesia. Finally, through the Monte Carlo method, they validate the effectiveness of the proposed approach on a general population, and the robustness to the intra- and inter-patient variability for different infusion balances.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11379/572072
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