Increasing Robustness of the Anesthesia Process from Difference Patient's Delay Using a State-Space Model Predictive Controller

TL;DR

This paper enhances anesthesia process robustness by integrating an Extended Kalman Filter with a state-space Model Predictive Controller to better handle patient delay variations.

Contribution

It introduces a combined EKF and MPC approach for anesthesia control, improving robustness against patient delay deviations.

Findings

The proposed controller outperforms standard MPC in handling patient delays.

Increased robustness in anesthesia depth control with the new method.

Effective estimation of drug concentration using EKF.

Abstract

The process of anesthesia is nonlinear with time delay and also there are some constraints which have to be considered in calculating administrative drug dosage. We present an Extended Kalman Filter (EKF) observer to estimate drug concentration in the patient's body and use this estimation in a state-space based Model of Predictive Controller (MPC) for controlling the depth of anesthesia. Bispectral Index (BIS) is used as a patient consciousness index and propofol as an anesthetic agent. Performance evaluations of the proposed controller, the results have been compared with those of a MPC controller. The results demonstrate that state-space MPC including the EKF estimator for controlling the anesthesia process can significantly increase the robustness in encountering patients' delay deviations in comparison with the MPC.