Constructing a control-ready model of EEG signal during general anesthesia in humans

TL;DR

This paper develops a control-ready EEG-based model for closed-loop anesthesia, capturing individual differences and enabling automated propofol delivery through a combination of PCA, logistic modeling, and nonlinear model predictive control.

Contribution

It introduces a novel patient model that links EEG signals, anesthetic dose, and individual sensitivity, facilitating closed-loop anesthesia control.

Findings

Successfully modeled EEG response to propofol using PCA and logistic functions.

Demonstrated in silico closed-loop control with nonlinear model predictive control.

Captured inter-individual variability in anesthetic sensitivity.

Abstract

Significant effort toward the automation of general anesthesia has been made in the past decade. One open challenge is in the development of control-ready patient models for closed-loop anesthesia delivery. Standard depth-of-anesthesia tracking does not readily capture inter-individual differences in response to anesthetics, especially those due to age, and does not aim to predict a relationship between a control input (infused anesthetic dose) and system state (commonly, a function of electroencephalography (EEG) signal). In this work, we developed a control-ready patient model for closed-loop propofol-induced anesthesia using data recorded during a clinical study of EEG during general anesthesia in ten healthy volunteers. We used principal component analysis to identify the low-dimensional state-space in which EEG signal evolves during anesthesia delivery. We parameterized the response of the EEG signal to changes in propofol target-site concentration using logistic models. We note that inter-individual differences in anesthetic sensitivity may be captured by varying a constant cofactor of the predicted effect-site concentration. We linked the EEG dose-response with the control input using a pharmacokinetic model. Finally, we present a simple nonlinear model predictive control in silico demonstration of how such a closed-loop system would work.