A Brain-Machine Interface for Control of Medically-Induced Coma
Medically-induced coma is a drug-induced state of profound brain inactivation and unconsciousness used to treat refractory intracranial hypertension and to manage treatment-resistant epilepsy. The state of coma is achieved by continually monitoring the patient's brain activity with an electroen...
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| 格式: | 文件 |
| 语言: | en_US |
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Public Library of Science
2013
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| 在线阅读: | http://hdl.handle.net/1721.1/82047 https://orcid.org/0000-0003-2668-7819 |
| _version_ | 1826206739918225408 |
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| author | Shanechi, Maryam M. Chemali, Jessica J. Liberman, Max Solt, Ken Brown, Emery N. |
| author2 | Institute for Medical Engineering and Science |
| author_facet | Institute for Medical Engineering and Science Shanechi, Maryam M. Chemali, Jessica J. Liberman, Max Solt, Ken Brown, Emery N. |
| author_sort | Shanechi, Maryam M. |
| collection | MIT |
| description | Medically-induced coma is a drug-induced state of profound brain inactivation and unconsciousness used to treat refractory intracranial hypertension and to manage treatment-resistant epilepsy. The state of coma is achieved by continually monitoring the patient's brain activity with an electroencephalogram (EEG) and manually titrating the anesthetic infusion rate to maintain a specified level of burst suppression, an EEG marker of profound brain inactivation in which bursts of electrical activity alternate with periods of quiescence or suppression. The medical coma is often required for several days. A more rational approach would be to implement a brain-machine interface (BMI) that monitors the EEG and adjusts the anesthetic infusion rate in real time to maintain the specified target level of burst suppression. We used a stochastic control framework to develop a BMI to control medically-induced coma in a rodent model. The BMI controlled an EEG-guided closed-loop infusion of the anesthetic propofol to maintain precisely specified dynamic target levels of burst suppression. We used as the control signal the burst suppression probability (BSP), the brain's instantaneous probability of being in the suppressed state. We characterized the EEG response to propofol using a two-dimensional linear compartment model and estimated the model parameters specific to each animal prior to initiating control. We derived a recursive Bayesian binary filter algorithm to compute the BSP from the EEG and controllers using a linear-quadratic-regulator and a model-predictive control strategy. Both controllers used the estimated BSP as feedback. The BMI accurately controlled burst suppression in individual rodents across dynamic target trajectories, and enabled prompt transitions between target levels while avoiding both undershoot and overshoot. The median performance error for the BMI was 3.6%, the median bias was -1.4% and the overall posterior probability of reliable control was 1 (95% Bayesian credibility interval of [0.87, 1.0]). A BMI can maintain reliable and accurate real-time control of medically-induced coma in a rodent model suggesting this strategy could be applied in patient care. |
| first_indexed | 2024-09-23T13:37:50Z |
| format | Article |
| id | mit-1721.1/82047 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T13:37:50Z |
| publishDate | 2013 |
| publisher | Public Library of Science |
| record_format | dspace |
| spelling | mit-1721.1/820472022-10-01T16:08:56Z A Brain-Machine Interface for Control of Medically-Induced Coma Shanechi, Maryam M. Chemali, Jessica J. Liberman, Max Solt, Ken Brown, Emery N. Institute for Medical Engineering and Science Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences Brown, Emery N. Medically-induced coma is a drug-induced state of profound brain inactivation and unconsciousness used to treat refractory intracranial hypertension and to manage treatment-resistant epilepsy. The state of coma is achieved by continually monitoring the patient's brain activity with an electroencephalogram (EEG) and manually titrating the anesthetic infusion rate to maintain a specified level of burst suppression, an EEG marker of profound brain inactivation in which bursts of electrical activity alternate with periods of quiescence or suppression. The medical coma is often required for several days. A more rational approach would be to implement a brain-machine interface (BMI) that monitors the EEG and adjusts the anesthetic infusion rate in real time to maintain the specified target level of burst suppression. We used a stochastic control framework to develop a BMI to control medically-induced coma in a rodent model. The BMI controlled an EEG-guided closed-loop infusion of the anesthetic propofol to maintain precisely specified dynamic target levels of burst suppression. We used as the control signal the burst suppression probability (BSP), the brain's instantaneous probability of being in the suppressed state. We characterized the EEG response to propofol using a two-dimensional linear compartment model and estimated the model parameters specific to each animal prior to initiating control. We derived a recursive Bayesian binary filter algorithm to compute the BSP from the EEG and controllers using a linear-quadratic-regulator and a model-predictive control strategy. Both controllers used the estimated BSP as feedback. The BMI accurately controlled burst suppression in individual rodents across dynamic target trajectories, and enabled prompt transitions between target levels while avoiding both undershoot and overshoot. The median performance error for the BMI was 3.6%, the median bias was -1.4% and the overall posterior probability of reliable control was 1 (95% Bayesian credibility interval of [0.87, 1.0]). A BMI can maintain reliable and accurate real-time control of medically-induced coma in a rodent model suggesting this strategy could be applied in patient care. National Institutes of Health (U.S.) (Director's Transformative Award R01 GM104948) National Institutes of Health (U.S.) (Pioneer Award DP1-OD003646) National Institutes of Health (U.S.) (NIH K08-GM094394) Massachusetts General Hospital. Dept. of Anesthesia and Critical Care 2013-11-08T15:55:50Z 2013-11-08T15:55:50Z 2013-10 2013-02 Article http://purl.org/eprint/type/JournalArticle 1553-7358 1553-734X http://hdl.handle.net/1721.1/82047 Shanechi, Maryam M., Jessica J. Chemali, Max Liberman, Ken Solt, and Emery N. Brown. “A Brain-Machine Interface for Control of Medically-Induced Coma.” Edited by Olaf Sporns. PLoS Computational Biology 9, no. 10 (October 31, 2013): e1003284. https://orcid.org/0000-0003-2668-7819 en_US http://dx.doi.org/10.1371/journal.pcbi.1003284 PLoS Computational Biology Creative Commons Attribution http://creativecommons.org/licenses/by/3.0/ application/pdf Public Library of Science PLOS |
| spellingShingle | Shanechi, Maryam M. Chemali, Jessica J. Liberman, Max Solt, Ken Brown, Emery N. A Brain-Machine Interface for Control of Medically-Induced Coma |
| title | A Brain-Machine Interface for Control of Medically-Induced Coma |
| title_full | A Brain-Machine Interface for Control of Medically-Induced Coma |
| title_fullStr | A Brain-Machine Interface for Control of Medically-Induced Coma |
| title_full_unstemmed | A Brain-Machine Interface for Control of Medically-Induced Coma |
| title_short | A Brain-Machine Interface for Control of Medically-Induced Coma |
| title_sort | brain machine interface for control of medically induced coma |
| url | http://hdl.handle.net/1721.1/82047 https://orcid.org/0000-0003-2668-7819 |
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