MPC for Propofol Anesthesia: the Noise Issue

Pawłowski, A.; Schiavo, Michele; Latronico, Nicola; Paltenghi, Massimiliano; Visioli, Antonio

doi:10.1109/ccta49430.2022.9966102

Cited by 8 publications

(7 citation statements)

References 25 publications

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“…The effect of noise is also considered. The noise signal already employed in [22], which has been extracted from real clinical data, is added to the BIS. The noise and disturbance signals are shown in Figure 5.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Then, T max has been set to 10 seconds (that is, n=10) because of safety reasons, as this ensures the minimal performance of the closed-loop control system [11]. Indeed, in a previous work [22], it was shown that the sampling period of 10 second is the maximum value that allows the clinical requirements to be met in terms of accuracy and disturbance compensation capability in the presence of measurement noise. Additionally, the value of β has been fixed to 5.…”

Section: Control System Tuningmentioning

confidence: 99%

“…These approaches usually results in a complex control system with heavy computational requirements and re-tuning or adaptation is not trivial [20]. From the clinical perspective they are also interesting since they can be used to design personalized control for an individual patient, by exploiting a parameterized patient model based on individual physiological characteristics like gender, weight, height and age to improve the prediction capability [3,21,22].…”

Section: Introductionmentioning

confidence: 99%

“…This problem can be partially addressed by placing a noise filter in the feedback-loop. However, the filtering action needs to be a compromise between noise attenuation and process dynamics modification [22]. Unfortunately, the same problem appears in the DoH control task and this issues is even more critical when the predictive control approaches are used.…”

Section: Introductionmentioning

confidence: 99%

“…Unfortunately, the same problem appears in the DoH control task and this issues is even more critical when the predictive control approaches are used. Taking into account this problem, an event-based control system can be used as a flexible alternative to classic time-based schemes as it can reduce the noise impact on the control action variability providing efficient use of control resources [22,23,25]. The application of an event-based paradigm enables the possibility to trigger the controller task basing on the controlled variable dynamics rather than a time progress, which is the main difference when compared to the classical time-based control system.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Event-based MPC for propofol administration in anesthesia

Pawłowski¹,

Schiavo²,

Latronico³

et al. 2023

Computer Methods and Programs in Biomedicine

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Control System Tuningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Event-based MPC for propofol administration in anesthesia

Pawłowski¹,

Schiavo²,

Latronico³

et al. 2023

Computer Methods and Programs in Biomedicine

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Drug co‐administration in anesthesia using event‐based MPC

Pawłowski,

Schiavo,

Latronico

et al. 2023

Intl J Robust & Nonlinear

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In this article, a predictive event‐based control architecture for multivariable drug infusion in the anesthesia process is presented. The control scheme considers a multiple‐input single‐output process, where the depth of hypnosis is the controlled variable and the infusion rates of propofol and remifentanil, which are co‐administered by imposing a fixed ratio between them, are the control variables. The control system is built on the top of a nonlinear pharmacokinetic/pharmacodynamic model for drug co‐administration that reflects the super‐additive effect of both drugs on the bispectral index scale (BIS), which is a measure of the hypnotic state of the patient. Further, in order to compensate the nonlinear behavior of the system, it exploits an external predictor that is designed to increase the robustness to inter‐/intra‐patient variability. The overall controller parameters are tuned by applying an optimization method on a representative set of virtual patients. The main feature of the proposed control algorithm consists in its asynchronous execution, which can be beneficial in reducing control signal changes due to the noisy measurements of the BIS. The evaluation of the analyzed control architecture through a simulation study reveals that control action variations can be reduced by about 48% on average with respect to its time‐based counterpart. This reduction is obtained at the expense of a lower control accuracy, resulting in a degradation of 18% in terms of integrated absolute error. Results are acceptable from a clinical practice perspective proving that the proposed approach is a feasible alternative to classical time‐based predictive controllers.

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