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A brain-machine interface for control of medically-induced coma.

Shanechi MM, Chemali JJ, Liberman M, Solt K, Brown EN - PLoS Comput. Biol. (2013)

Bottom Line: 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 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.

View Article: PubMed Central - PubMed

Affiliation: School of Electrical and Computer Engineering, Cornell University, Ithaca, New York, United States of America ; Department of Electrical Engineering and Computer Science, University of California, Berkeley, California, United States of America.

ABSTRACT
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.

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Simulated closed-loop controlled BSP traces.In each subfigure, the top panel shows the BSP traces and the bottom panel shows the drug infusion rate. In the top panels, sample trials of the closed-loop controlled BSP traces are shown in black and the corresponding estimated BSP traces are shown in grey. The time-varying target BSP level is shown in green. The bottom panel shows the corresponding controller infusion rates. Each subfigure (a–f) corresponds to one possible permutation of the 3 BSP target levels.
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pcbi-1003284-g003: Simulated closed-loop controlled BSP traces.In each subfigure, the top panel shows the BSP traces and the bottom panel shows the drug infusion rate. In the top panels, sample trials of the closed-loop controlled BSP traces are shown in black and the corresponding estimated BSP traces are shown in grey. The time-varying target BSP level is shown in green. The bottom panel shows the corresponding controller infusion rates. Each subfigure (a–f) corresponds to one possible permutation of the 3 BSP target levels.

Mentions: Figure 3 shows sample closed-loop controlled BSP traces for each of the 6 possible permutations of the desired target trajectories. Here the only imposed constraint is positivity of the drug infusion rates. In each subfigure, the top panel shows the BSP traces and the bottom panel shows the drug infusion rate. The stochastic control framework can achieve successful control of burst-suppression. The framework is particularly successful in changing the BSP level without overshoot or undershoot.


A brain-machine interface for control of medically-induced coma.

Shanechi MM, Chemali JJ, Liberman M, Solt K, Brown EN - PLoS Comput. Biol. (2013)

Simulated closed-loop controlled BSP traces.In each subfigure, the top panel shows the BSP traces and the bottom panel shows the drug infusion rate. In the top panels, sample trials of the closed-loop controlled BSP traces are shown in black and the corresponding estimated BSP traces are shown in grey. The time-varying target BSP level is shown in green. The bottom panel shows the corresponding controller infusion rates. Each subfigure (a–f) corresponds to one possible permutation of the 3 BSP target levels.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3814408&req=5

pcbi-1003284-g003: Simulated closed-loop controlled BSP traces.In each subfigure, the top panel shows the BSP traces and the bottom panel shows the drug infusion rate. In the top panels, sample trials of the closed-loop controlled BSP traces are shown in black and the corresponding estimated BSP traces are shown in grey. The time-varying target BSP level is shown in green. The bottom panel shows the corresponding controller infusion rates. Each subfigure (a–f) corresponds to one possible permutation of the 3 BSP target levels.
Mentions: Figure 3 shows sample closed-loop controlled BSP traces for each of the 6 possible permutations of the desired target trajectories. Here the only imposed constraint is positivity of the drug infusion rates. In each subfigure, the top panel shows the BSP traces and the bottom panel shows the drug infusion rate. The stochastic control framework can achieve successful control of burst-suppression. The framework is particularly successful in changing the BSP level without overshoot or undershoot.

Bottom Line: 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 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.

View Article: PubMed Central - PubMed

Affiliation: School of Electrical and Computer Engineering, Cornell University, Ithaca, New York, United States of America ; Department of Electrical Engineering and Computer Science, University of California, Berkeley, California, United States of America.

ABSTRACT
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.

Show MeSH
Related in: MedlinePlus