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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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System identification.(a) and (b) show two sample fitted system responses. The measured BSP trace in response to a preliminary bolus of propofol is shown in grey and the response of the second-order system model in (2) fitted using nonlinear least-squares is shown in red.
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pcbi-1003284-g002: System identification.(a) and (b) show two sample fitted system responses. The measured BSP trace in response to a preliminary bolus of propofol is shown in grey and the response of the second-order system model in (2) fitted using nonlinear least-squares is shown in red.

Mentions: We use a stochastic optimal control paradigm to design a real-time BMI to control medical coma using burst suppression (Figure 1a). As our measure of the burst suppression level, we use the burst suppression probability (BSP), a number between 0 and 1, which defines the instantaneous probability of the EEG being suppressed. The BSP is computed in one-second intervals in real time by filtering and thresholding the EEG to convert it into binary observations (Figure 1b). To estimate the BSP from the binary observations we first formulate a two-dimensional compartment model that relates the BSP to the concentrations of the anesthetic in the central compartment and the effect site compartment (Figure 1c). We next estimate the parameters of the compartment model based on the EEG observations recorded in a systems identification experiment conducted prior to initiating real-time control (Figure 2). We carry out our stochastic control framework by developing from the two-dimensional compartment model a recursive Bayesian estimator of the concentration states and consequently of the BSP from the binary observations in real time (14)–(17). We develop a LQR controller that takes the concentration estimates as feedback and determines the drug infusion rate in real time (25). In addition to the LQR control strategy, we also implement a model predictive controller (MPC) that allows us to explicitly impose constraints on the anesthetic infusion rates (27). We present the mathematical details of the system identification, formulation of the Bayesian estimator and the two controllers for the interested readers below. These mathematical details in this subsection are not necessary to follow the remainder of the paper beginning with the Results.


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

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

System identification.(a) and (b) show two sample fitted system responses. The measured BSP trace in response to a preliminary bolus of propofol is shown in grey and the response of the second-order system model in (2) fitted using nonlinear least-squares is shown in red.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1003284-g002: System identification.(a) and (b) show two sample fitted system responses. The measured BSP trace in response to a preliminary bolus of propofol is shown in grey and the response of the second-order system model in (2) fitted using nonlinear least-squares is shown in red.
Mentions: We use a stochastic optimal control paradigm to design a real-time BMI to control medical coma using burst suppression (Figure 1a). As our measure of the burst suppression level, we use the burst suppression probability (BSP), a number between 0 and 1, which defines the instantaneous probability of the EEG being suppressed. The BSP is computed in one-second intervals in real time by filtering and thresholding the EEG to convert it into binary observations (Figure 1b). To estimate the BSP from the binary observations we first formulate a two-dimensional compartment model that relates the BSP to the concentrations of the anesthetic in the central compartment and the effect site compartment (Figure 1c). We next estimate the parameters of the compartment model based on the EEG observations recorded in a systems identification experiment conducted prior to initiating real-time control (Figure 2). We carry out our stochastic control framework by developing from the two-dimensional compartment model a recursive Bayesian estimator of the concentration states and consequently of the BSP from the binary observations in real time (14)–(17). We develop a LQR controller that takes the concentration estimates as feedback and determines the drug infusion rate in real time (25). In addition to the LQR control strategy, we also implement a model predictive controller (MPC) that allows us to explicitly impose constraints on the anesthetic infusion rates (27). We present the mathematical details of the system identification, formulation of the Bayesian estimator and the two controllers for the interested readers below. These mathematical details in this subsection are not necessary to follow the remainder of the paper beginning with the Results.

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