  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.

Neurons and Cognition