Propofol Anesthesia; Regulation of Hypnosis using Nonlinear Control Techniques

Abstract

Advance technologies and engineering applications have played a significant role in the design and improvement of clinical procedures during the last few decades. Control of drug infusion for patients health and safety is one of the most important step during surgeries. The main objective of safe anesthesia delivery is to achieve the optimum dosage during surgery and simultaneously taking into account the patient clinical parameters and drug requirements. Continuous administration of drug infusion during surgical procedures is essential but increases the undue load of an anesthetist in operating room working in a multi-tasking setup. Manual and target controlled infusion (TCI) systems are not good at handling disturbances or instabilities arising due to inter-patient variability. Patient safety, large interindividual variability and less post-operative effects are the main factors to motivate automation in anesthesia. The idea of automated system for drug (Propofol) infusion excites the control engineers to come up with a more sophisticated and safe system that handles optimum delivery of drug during surgery and avoid post-operative effects. While most of the work done in anesthesia infusion systems are with linear control strategies, like PID (Proportional Integral derivative), IMC (Internal Model Control) and LMPC (Linear Model Predictive Control) or their improved variants but these linear control methods are not good at handling disturbances and uncertainties related to the system dynamics. These disturbances, which includes, heart rate variability, blood pressure changes and muscular movement, are the main issues causing complexities during surgical activities. The novelty and originality of this research work lies in employing nonlinear control techniques i.e., Sliding Mode Control (SMC) and Backstepping, to regulate the desired hypnosis level of patients undergoing surgery. These two methods, in our knowledge, are not yet applied on anesthesia infusion systems for hypnosis regulation. Both of these control strategies are capable of handling uncertainties and inter-patient variability arising due to the differences in patients clinical data. Simulation results from these methods are analyzed in detail for hypnosis level of the patients and for plasma-drug concentration as well. This effort is envisioned to unleash the true potentials of these nonlinear control techniques for anesthesia systems used today in biomedical field. Results obtained from these non-linear control methods, in terms of hypnosis level of patients are better than linear control methods. A non-linear control strategy, Sliding Mode Control (SMC), possesses outstanding characteristics related to robustness, accuracy and implementation. Control of nonlinear processes with different types of external disturbances and model uncertainties is one of the practical advantage of this method. This non-linear control method can be applied to a wide class of non-linear systems however, their application is limited to single input systems. For the sake of research and analysis, we expand this work to more advanced control technique i.e., backstepping, for hypnosis level tracking. It is a recursive design procedure used for designing stabilizing control for the class of nonlinear dynamical systems. The performance of the designed control laws are studied on the real dataset of (8 for SMC and 5 for backstepping) patients undergoing surgery with different clinical parameters. Despite large patient variability (which includes inter-patient and intra-patient variability), the controller regulates the desired hypnosis level of all patient within the acceptable range as specified by BIS (Bispectral Index Scale) without overdose for smooth conduction of surgery.