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 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 Car

Dynamic Assessment of Baroreflex Control of Heart Rate During Induction of Propofol Anesthesia Using a Point Process Method

In this article, we present a point process method to assess dynamic baroreflex sensitivity (BRS) by estimating the baroreflex gain as focal component of a simplified closed-loop model of the cardiovascular system. Specifically, an inverse Gaussian probability distribution is used to model the heartbeat interval, whereas the instantaneous mean is identified by linear and bilinear bivariate regressions on both the previous R−R intervals (RR) and blood pressure (BP) beat-to-beat measures. The instantaneous baroreflex gain is estimated as the feedback branch of the loop with a point-process filter, while the RRBP feedforward transfer function representing heart contractility and vasculature effects is simultaneously estimated by a recursive least-squares filter. These two closed-loop gains provide a direct assessment of baroreflex control of heart rate (HR). In addition, the dynamic coherence, cross bispectrum, and their power ratio can also be estimated. All statistical indices provide a valuable quantitative assessment of the interaction between heartbeat dynamics and hemodynamics. To illustrate the application, we have applied the proposed point process model to experimental recordings from 11 healthy subjects in order to monitor cardiovascular regulation under propofol anesthesia. We present quantitative results during transient periods, as well as statistical analyses on steady-state epochs before and after propofol administration. Our findings validate the ability of the algorithm to provide a reliable and fast-tracking assessment of BRS, and show a clear overall reduction in baroreflex gain from the baseline period to the start of propofol anesthesia, confirming that instantaneous evaluation of arterial baroreflex control of HR may yield important implications in clinical practice, particularly during anesthesia and in postoperative care.National Institutes of Health (U.S.) (Grant R01-HL084502)National Institutes of Health (U.S.) (Grant K25-NS05758)National Institutes of Health (U.S.) (Grant DP2- OD006454)National Institutes of Health (U.S.) (Grant T32NS048005)National Institutes of Health (U.S.) (Grant T32NS048005)National Institutes of Health (U.S.) (Grant R01-DA015644)Massachusetts General Hospital (Clinical Research Center, UL1 Grant RR025758

An open source patient simulator for design and evaluation of computer based multiple drug dosing control for anesthetic and hemodynamic variables

We are witnessing a notable rise in the translational use of information technology and control systems engineering tools in clinical practice. This paper empowers the computer based drug dosing optimization of general anesthesia management by means of multiple variables for patient state stabilization. The patient simulator platform is designed through an interdisciplinary combination of medical, clinical practice and systems engineering expertise gathered in the last decades by our team. The result is an open source patient simulator in Matlab/Simulink from Mathworks(R). Simulator features include complex synergic and antagonistic interaction aspects between general anesthesia and hemodynamic stabilization variables. The anesthetic system includes the hypnosis, analgesia and neuromuscular blockade states, while the hemodynamic system includes the cardiac output and mean arterial pressure. Nociceptor stimulation is also described and acts as a disturbance together with predefined surgery profiles from a translation into signal form of most commonly encountered events in clinical practice. A broad population set of pharmacokinetic and pharmacodynamic (PKPD) variables are available for the user to describe both intra- and inter-patient variability. This simulator has some unique features, such as: i) additional bolus administration from anesthesiologist, ii) variable time-delays introduced by data window averaging when poor signal quality is detected, iii) drug trapping from heterogeneous tissue diffusion in high body mass index patients. We successfully reproduced the clinical expected effects of various drugs interacting among the anesthetic and hemodynamic states. Our work is uniquely defined in current state of the art and first of its kind for this application of dose management problem in anesthesia. This simulator provides the research community with accessible tools to allow a systematic design, evaluation and comparison of various control algorithms for multi-drug dosing optimization objectives in anesthesia

On Automation in Anesthesia

The thesis discusses closed-loop control of the hypnotic and the analgesic components of anesthesia. The objective of the work has been to develop a system which independently controls the intravenous infusion rates of the hypnotic drug propofol and analgesic drug remifentanil. The system is designed to track a reference hypnotic depth level, while maintaining adequate analgesia. This is complicated by inter-patient variability in drug sensitivity, disturbances caused foremost by surgical stimulation, and measurement noise. A commercially available monitor is used to measure the hypnotic depth of the patient, while a simple soft sensor estimates the analgesic depth. Both induction and maintenance of anesthesia are closed-loop controlled, using a PID controller for propofol and a P controller for remifentanil. In order to tune the controllers, patient models have been identified from clinical data, with body mass as only biometric parameter. Care has been taken to characterize identifiability and produce models which are safe for the intended application. A scheme for individualizing the controller tuning upon completion of the induction phase of anesthesia is proposed. Practical aspects such as integrator anti-windup and loss of the measurement signal are explicitly addressed. The validity of the performance measures, most commonly reported in closed-loop anesthesia studies, is debated and a new set of measures is proposed. It is shown, both in simulation and clinically, that PID control provides a viable approach. Both results from simulations and clinical trials are presented. These results suggest that closed-loop controlled anesthesia can be provided in a safe and efficient manner, relieving the regulatory and server controller role of the anesthesiologist. However, outlier patient dynamics, unmeasurable disturbances and scenarios which are not considered in the controller synthesis, urge the presence of an anesthesiologist. Closed-loop controlled anesthesia should therefore not be viewed as a replacement of human expertise, but rather as a tool, similar to the cruise controller of a car

Design and clinical evaluation of robust PID control of propofol anesthesia in children

This paper describes the design of a robust PID controller for propofol infusion in children and presents the results of clinical evaluation of this closed-loop system during endoscopic investigations in children age 6y-17y. The controller design is based on a set of models that describes the inter- patient variability in the response to propofol infusion in the study population. The PID controller is tuned to achieve sufficient robustness margins for the identified uncertainty. 108 children were enrolled in the study, anesthesia was closed-loop controlled in 102 of these cases. Clinical evaluation of the system shows that closed-loop control of both induction and maintenance of anesthesia in children based on the WAVCNS index as a measure of clinical effect is feasible. A robustly tuned PID controller can accommodate the inter-patient variability in children and spontaneous breathing can be maintained in most subjects