A closed-loop anesthetic delivery system for real-time control of burst suppression
Objective. There is growing interest in using closed-loop anesthetic delivery (CLAD) systems to automate control of brain states (sedation, unconsciousness and antinociception) in patients receiving anesthesia care. The accuracy and reliability of these systems can be improved by using as control si...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | en_US |
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IOP Publishing
2016
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| Online Access: | http://hdl.handle.net/1721.1/102346 https://orcid.org/0000-0003-2668-7819 |
| _version_ | 1826211032566071296 |
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| author | Chemali, Jessica Liberman, Max Y. Ching, Shinung Brown, Emery Neal |
| author2 | Massachusetts Institute of Technology. Institute for Medical Engineering & Science |
| author_facet | Massachusetts Institute of Technology. Institute for Medical Engineering & Science Chemali, Jessica Liberman, Max Y. Ching, Shinung Brown, Emery Neal |
| author_sort | Chemali, Jessica |
| collection | MIT |
| description | Objective. There is growing interest in using closed-loop anesthetic delivery (CLAD) systems to automate control of brain states (sedation, unconsciousness and antinociception) in patients receiving anesthesia care. The accuracy and reliability of these systems can be improved by using as control signals electroencephalogram (EEG) markers for which the neurophysiological links to the anesthetic-induced brain states are well established. Burst suppression, in which bursts of electrical activity alternate with periods of quiescence or suppression, is a well-known, readily discernible EEG marker of profound brain inactivation and unconsciousness. This pattern is commonly maintained when anesthetics are administered to produce a medically-induced coma for cerebral protection in patients suffering from brain injuries or to arrest brain activity in patients having uncontrollable seizures. Although the coma may be required for several hours or days, drug infusion rates are managed inefficiently by manual adjustment. Our objective is to design a CLAD system for burst suppression control to automate management of medically-induced coma. Approach. We establish a CLAD system to control burst suppression consisting of: a two-dimensional linear system model relating the anesthetic brain level to the EEG dynamics; a new control signal, the burst suppression probability (BSP) defining the instantaneous probability of suppression; the BSP filter, a state-space algorithm to estimate the BSP from EEG recordings; a proportional–integral controller; and a system identification procedure to estimate the model and controller parameters. Main results. We demonstrate reliable performance of our system in simulation studies of burst suppression control using both propofol and etomidate in rodent experiments based on Vijn and Sneyd, and in human experiments based on the Schnider pharmacokinetic model for propofol. Using propofol, we further demonstrate that our control system reliably tracks changing target levels of burst suppression in simulated human subjects across different epidemiological profiles. Significance. Our results give new insights into CLAD system design and suggest a control-theory framework to automate second-to-second control of burst suppression for management of medically-induced coma. |
| first_indexed | 2024-09-23T14:59:29Z |
| format | Article |
| id | mit-1721.1/102346 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T14:59:29Z |
| publishDate | 2016 |
| publisher | IOP Publishing |
| record_format | dspace |
| spelling | mit-1721.1/1023462022-10-01T23:48:52Z A closed-loop anesthetic delivery system for real-time control of burst suppression Chemali, Jessica Liberman, Max Y. Ching, Shinung Brown, Emery Neal Massachusetts Institute of Technology. Institute for Medical Engineering & Science Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences Ching, ShiNung Brown, Emery N. Objective. There is growing interest in using closed-loop anesthetic delivery (CLAD) systems to automate control of brain states (sedation, unconsciousness and antinociception) in patients receiving anesthesia care. The accuracy and reliability of these systems can be improved by using as control signals electroencephalogram (EEG) markers for which the neurophysiological links to the anesthetic-induced brain states are well established. Burst suppression, in which bursts of electrical activity alternate with periods of quiescence or suppression, is a well-known, readily discernible EEG marker of profound brain inactivation and unconsciousness. This pattern is commonly maintained when anesthetics are administered to produce a medically-induced coma for cerebral protection in patients suffering from brain injuries or to arrest brain activity in patients having uncontrollable seizures. Although the coma may be required for several hours or days, drug infusion rates are managed inefficiently by manual adjustment. Our objective is to design a CLAD system for burst suppression control to automate management of medically-induced coma. Approach. We establish a CLAD system to control burst suppression consisting of: a two-dimensional linear system model relating the anesthetic brain level to the EEG dynamics; a new control signal, the burst suppression probability (BSP) defining the instantaneous probability of suppression; the BSP filter, a state-space algorithm to estimate the BSP from EEG recordings; a proportional–integral controller; and a system identification procedure to estimate the model and controller parameters. Main results. We demonstrate reliable performance of our system in simulation studies of burst suppression control using both propofol and etomidate in rodent experiments based on Vijn and Sneyd, and in human experiments based on the Schnider pharmacokinetic model for propofol. Using propofol, we further demonstrate that our control system reliably tracks changing target levels of burst suppression in simulated human subjects across different epidemiological profiles. Significance. Our results give new insights into CLAD system design and suggest a control-theory framework to automate second-to-second control of burst suppression for management of medically-induced coma. National Institutes of Health (U.S.) (Award DP1-OD003646) National Institutes of Health (U.S.) (Award R01GM10498) Burroughs Wellcome Fund (Award 1010625) 2016-05-02T14:05:10Z 2016-05-02T14:05:10Z 2013-06 2013-03 Article http://purl.org/eprint/type/JournalArticle 1741-2560 1741-2552 http://hdl.handle.net/1721.1/102346 Liberman, Max Y, ShiNung Ching, Jessica Chemali, and Emery N Brown. “A Closed-Loop Anesthetic Delivery System for Real-Time Control of Burst Suppression.” Journal of Neural Engineering 10, no. 4 (June 7, 2013): 046004. https://orcid.org/0000-0003-2668-7819 en_US http://dx.doi.org/10.1088/1741-2560/10/4/046004 Journal of Neural Engineering Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf IOP Publishing PMC |
| spellingShingle | Chemali, Jessica Liberman, Max Y. Ching, Shinung Brown, Emery Neal A closed-loop anesthetic delivery system for real-time control of burst suppression |
| title | A closed-loop anesthetic delivery system for real-time control of burst suppression |
| title_full | A closed-loop anesthetic delivery system for real-time control of burst suppression |
| title_fullStr | A closed-loop anesthetic delivery system for real-time control of burst suppression |
| title_full_unstemmed | A closed-loop anesthetic delivery system for real-time control of burst suppression |
| title_short | A closed-loop anesthetic delivery system for real-time control of burst suppression |
| title_sort | closed loop anesthetic delivery system for real time control of burst suppression |
| url | http://hdl.handle.net/1721.1/102346 https://orcid.org/0000-0003-2668-7819 |
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