Constructing a control-ready model of EEG signal during general anesthesia in humans
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 i...
| Main Authors: | , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Elsevier BV
2021
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| Online Access: | https://hdl.handle.net/1721.1/138185 |
| _version_ | 1826196004468162560 |
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| author | Abel, John H Badgeley, Marcus A Baum, Taylor E Chakravarty, Sourish Purdon, Patrick L Brown, Emery N |
| author2 | Picower Institute for Learning and Memory |
| author_facet | Picower Institute for Learning and Memory Abel, John H Badgeley, Marcus A Baum, Taylor E Chakravarty, Sourish Purdon, Patrick L Brown, Emery N |
| author_sort | Abel, John H |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T10:19:19Z |
| format | Article |
| id | mit-1721.1/138185 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T10:19:19Z |
| publishDate | 2021 |
| publisher | Elsevier BV |
| record_format | dspace |
| spelling | mit-1721.1/1381852024-03-19T14:22:23Z Constructing a control-ready model of EEG signal during general anesthesia in humans Abel, John H Badgeley, Marcus A Baum, Taylor E Chakravarty, Sourish Purdon, Patrick L Brown, Emery N 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 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. 2021-11-22T16:55:19Z 2021-11-22T16:55:19Z 2020 2021-11-22T16:50:58Z Article http://purl.org/eprint/type/ConferencePaper https://hdl.handle.net/1721.1/138185 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). en 10.1016/J.IFACOL.2020.12.243 IFAC-PapersOnLine Creative Commons Attribution-NonCommercial-NoDerivs License http://creativecommons.org/licenses/by-nc-nd/4.0/ application/pdf Elsevier BV Elsevier |
| spellingShingle | Abel, John H Badgeley, Marcus A Baum, Taylor E Chakravarty, Sourish Purdon, Patrick L Brown, Emery N Constructing a control-ready model of EEG signal during general anesthesia in humans |
| title | Constructing a control-ready model of EEG signal during general anesthesia in humans |
| title_full | Constructing a control-ready model of EEG signal during general anesthesia in humans |
| title_fullStr | Constructing a control-ready model of EEG signal during general anesthesia in humans |
| title_full_unstemmed | Constructing a control-ready model of EEG signal during general anesthesia in humans |
| title_short | Constructing a control-ready model of EEG signal during general anesthesia in humans |
| title_sort | constructing a control ready model of eeg signal during general anesthesia in humans |
| url | https://hdl.handle.net/1721.1/138185 |
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