Continuous monitoring of electroencephalogram (EEG) recordings in humans under general anesthesia (GA) has demonstrated that changes in EEG dynamics induced by an anesthetic drug are reliably associated with the altered arousal states caused by the drug. This observation suggests that an intelligent, closed-loop anesthesia delivery (CLAD) system operating in real-time could track EEG dynamics and control the infusion rate of a programmable pump to precisely maintain unconsciousness. The United States FDA acknowledges the potential benefits of such automatic physiological closed-loop control devices for patient care. Bringing these devices into clinical practice requires establishing their feasibility in suitable animal models. Therefore, given the close neurophysiological proximity between human and non-human primates (NHPs), we address this problem by developing and validating a propofol CLAD system in rhesus macaques. Our CLAD system has three key components: (1) a data acquisition system that records cortical local field potentials (LFPs) from an NHP in real-time; (2) a computer executing our CLAD algorithm that takes in the LFP signals as input and outputs infusion rates; and (3) a computer-controlled infusion pump that administers intravenous propofol. Our CLAD system controls an empirically-determined LFP marker of unconsciousness (MOU) at a user-prescribed target value by updating every 20 seconds the propofol infusion rate based on real-time processing of the LFP signal. The MOU is the instantaneous power in the 20 to 30 Hz band of the LFP spectrogram. Every cycle (duration ~ 20 sec), our CLAD algorithm updates the MOU estimate and uses a robust optimal control strategy to adjust the propofol infusion rate based on the instantaneous error. This error is computed as the difference between the current and the user-prescribed target MOU values. Using neural recordings from multiple NHP anesthesia sessions, we first established that our chosen MOU signal was strongly correlated with propofol-induced decreased spiking activity which itself has been shown earlier to be associated with the level of unconsciousness in NHPs. Then we designed robust optimal control strategies that used subject-specific pharmacokinetic-pharmacodynamic models describing the MOU dynamics due to propofol infusion rate changes. Finally, we achieved safe and efficient closed-loop control of level of unconsciousness in 9 CLAD experiments involving 2 NHPs and 2 different 125 min long target MOU profiles with three target MOU changes within a given experiment. Our CLAD system performs stably, accurately and robustly across a total of 1125 min of closed-loop control. The CLAD performance measures, represented as median (25th percentile, 75th percentile), are 3.13 % (2.62%, 3.53%) for inaccuracy, 0.54 %(-0.31%, 0.89%) for bias, -0.02%/min (-0.06%/min, 0.00%/min) for divergence, and 3% (2.49%, 3.59%) for wobble. These performance measures were comparable or superior to previously reported CLAD performance measures from clinical studies (conducted outside USA) as well as rodent-based studies. The key innovations here are: (1) a pre-clinical NHP model for CLAD development and testing, (2) a neuroscience-informed LFP-based MOU for CLAD, (3) parsimonious, pharmacology-informed models to describe MOU dynamics under propofol infusion in rhesus macaques, (4) a novel numerical testing framework for propofol CLAD that incorporates a principled optimal robust control strategy for titrating propofol, and finally (5) experimental findings demonstrating the feasibility of stable, accurate and robust CLAD in the NHP model. Our NHP-based CLAD framework provides a principled pre-clinical research platform that can form the foundation for future clinical studies.
Stochastic Optimal Control as a Theory of Brain-Machine Interface Operation
