A Brain-Machine Interface for Control of Medically-Induced Coma

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.

Robust control of burst suppression for medical coma

“…An improvement of the BSP estimation algorithm presented herein over a previous design [37] is that the current version employs a sigmoidal function (Hill equation) to capture patient pharmacodynamics. The logistic link function used in previous work λ t = (1 − exp(− x 4 t )/(1 + exp(− x 4 t ) has a maximum slope at an effect site concentration of x 4 t = 0.…”

“…Our burst suppression segmentation algorithm identifies bursts and suppressions in human EEG recordings with a performance comparable to human experts [20]. We have also successfully used this algorithm previously in two sets of burst suppression CLAD control experiments in animals [37, 48]. In the remainder of this work we therefore treat the binary observations as given.…”

“…Instead, we use the BSP, a statistically rigorous measure of burst suppression depth [18]. We have used the BSP in our prior CLAD work for medical coma [37, 48]. …”

“…Many control paradigms are available, including proportional-integral-derivative, LQR, and MPC frameworks used in previous animal experiments by our group [37, 48], as well as nonlinear and adaptive methods. In the present work we employ a standard PI controller.…”

“…Several CLAD system designs for managing medical coma have previously been proposed and tested in computer simulations and animal models [17, 37, 47]. Early controllers were based on ad hoc designs, and, reported average rather than individualized results, and considered only constant target levels of anesthesia without investigating controller behavior in response to changes in target burst suppression ratio (BSR) values [17].…”