Modeling, estimation and control of the anaesthesia process

• Published in: Computers & chemical engineering Vol. 107; pp. 318 - 332
• Main Authors: Nașcu, Ioana, Pistikopoulos, Efstratios N.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 05.12.2017
• Subjects: Compartmental modeling; Estimation; Inter/-intra patient variability; Intravenous anaesthesia; Mutiparameric/explicit model predictive control; Compartmental modeling; Inter/-intra patient variability; Mutiparameric/explicit model predictive control; Intravenous anaesthesia; Estimation
• ISSN: 0098-1354; 1873-4375
• DOI: 10.1016/j.compchemeng.2017.02.016

•A mathematical model for the drug distribution and drug effect of intravenous anaesthesia was discussed.•Different estimation techniques have been designed, implemented and tested: Kalman filter, offline MHE and online MHE.•The state estimators have been implemented simultaneously with mp-MPC and simulated comparatively.•The developed strategies were tested both with and without noise influencing the output.•Two main challenges in the control of DOA: nonlinearity and inter-and intra- patient variability are successfully addressed. In this work we present different design strategies towards model based simultaneous multiparametric model predictive control and state estimation for intravenous anaesthesia. We first present a detailed compartmental mathematical model featuring a pharmacokinetic and a pharmacodynamics part. Due to unavailability of data and information, different estimation techniques are formulated and implemented. Furthermore these estimation techniques are implemented simultaneously with multiparametric model predictive controllers and tested for real patient data under the assumption that the output is either noise free or corrupted by noise. The derived control schemes are able to deal with two of the main challenges in controlling the depth of anaesthesia: (i) model nonlinearity and (ii) inter- and intra- patient variability.