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.
Highlights
Medically-induced coma is a drug-induced state of profound brain inactivation and unconsciousness used to treat refractory intracranial hypertension and status epilepticus, i.e., epilepsy that is refractory to standard medical therapies [1,2,3]
To design the closed-loop brain-machine interface (BMI), we present a certainty-equivalent optimal feedback control approach [8] by deriving an estimator for the burst suppression state based on the EEG observations and designing an optimal feedback controller that takes this estimate as a feedback signal to control the drug infusion rate in real time (Figure 1a)
To study the feasibility of automating control of medicallyinduced coma, we developed a BMI to control burst suppression in a rodent model
Summary
Medically-induced coma ( referred to as medical coma) is a drug-induced state of profound brain inactivation and unconsciousness used to treat refractory intracranial hypertension and status epilepticus, i.e., epilepsy that is refractory to standard medical therapies [1,2,3]. In the treatment of status epilepticus the anesthetic is administered to directly inhibit activity in the seizure foci [3]. For treating both refractory intracranial hypertension and status epilepticus, the state of medical coma is achieved by continually monitoring the patient’s brain activity with the electroencephalogram (EEG) and titrating the anesthetic drug infusion rate to maintain a specified level of burst suppression. Once burst suppression is achieved, it can be controlled by decreasing or increasing the infusion rate of the anesthetic to decrease or increase the suppression level
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