A model predictive control strategy for the regulation of hypnosis

Abstract

The “anesthetic state” is a dynamic combination of hypnosis, amnesia, analgesia, neuromuscular and neurohumoral blockade. To achieve this state, different combinations of drug effects must be induced in the patient undergoing surgical procedure under anesthesia. Patients are routinely and extensively monitored while under anesthesia. This makes it easy to observe and quantify the effects of anesthetic drugs and also to establish the relations between the drugs administered, degree of effect and the factors that can influence this relation. This work focuses on the problem of controlling the hypnosis during an anesthesia process by automatic regulation of a hypnotic drug. We assume that the remaining anesthetic states are regulated by manual administration of specific drugs. Past research in this area has employed the cascade control strategy for the automatic control of hypnosis by using the bispectral index (BIS) as the primary controlled variable and drug concentration as the second-dary controlled variable. Note that the BIS is an indirect measure of the hypnosis. We use a nonlinear pharmacokinetic-pharmacodynamic representation of the hypnosis process dynamics and propose a model predictive control (MPC) structure for controlling the BIS. This strategy has found many applications in chemical process control and has potential for medical applications. Further, the constraints imposed upon the variables and saturation limits imposed on the actuators are very well taken care of by the MPC strategy. This controller also performed well even when one of the measured feedback signals failed. The results obtained from the MPC strategy are compared with the other control strategies (adaptive control strategy with modeling error compensation) reported in the literature. This article gives an insight into the application of different control strategies to the automatic regulation of hypnosis using BIS as the controlled variable.