Constructing a control-ready model of EEG signal during general anesthesia in humans
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1-s2.0-S2405896320305188-main.pdf
Description
Published version
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1.08 MB
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Adobe PDF
Checksum (MD5)
eb1f827d99f11a1b31089c4fd66379ec
Author(s)
Abel, John H
Badgeley, Marcus A
Baum, Taylor E
Chakravarty, Sourish
Purdon, Patrick L
Brown, Emery Neal
Date Issued
2020
Journal
IFAC-PapersOnLine
Publisher
Elsevier BV
Citation
Abel, John H, Badgeley, Marcus A, Baum, Taylor E, Chakravarty, Sourish, Purdon, Patrick L et al. 2020. "Constructing a control-ready model of EEG signal during general anesthesia in humans." IFAC-PapersOnLine, 53 (2).
Version
Final published version
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.
MIT Department
Picower Institute for Learning and Memory
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Massachusetts Institute of Technology. Institute for Medical Engineering & Science
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/J.IFACOL.2020.12.243
