Constructing a control-ready model of EEG signal during general anesthesia in humans
Autor: | Taylor E. Baum, John H. Abel, Patrick L. Purdon, Sourish Chakravarty, Marcus A. Badgeley, Emery N. Brown |
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Jazyk: | angličtina |
Rok vydání: | 2020 |
Předmět: |
0209 industrial biotechnology
medicine.diagnostic_test Computer science 020208 electrical & electronic engineering Patient model 02 engineering and technology Nonlinear control Electroencephalography Signal Article Model predictive control 020901 industrial engineering & automation Control and Systems Engineering FOS: Biological sciences Anesthesia Quantitative Biology - Neurons and Cognition Anesthetic 0202 electrical engineering electronic engineering information engineering medicine Neurons and Cognition (q-bio.NC) Sensitivity (control systems) Propofol medicine.drug |
Zdroj: | Proc IFAC World Congress Elsevier |
Popis: | 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. 7 pages, 6 figures. This work has been submitted to IFAC for possible publication |
Databáze: | OpenAIRE |
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