Individualized closed-loop anesthesia through patient model partitioning

Sammanfattning: This master thesis project proposes methods for individualizing closed-loop controlled anesthesia. One of the largest challenges with closed-loop anesthesia is the variation between patients in the sensitivity to the anesthetic drug, here propofol. Due to limited excitation in the process dynamics together with a high measurement noise level is it not possible to determine a full reliable model describing a patient’s dynamics online. The method used here for minimizing the effects of inter-patient variability was through patient model partitioning of children and adult models. Partitioning was based on similarity measures between patients, for example age, weight and applied to a dynamic model describing each patient. For each subset resulting from partitioning, an optimal PID controller has been synthesized. This thesis has shown that the effects of inter-patient variability can be reduced using partitioning into two subsets. More subsets did not result in a significant reduction. Partitioning based on n-gap between patient models resulted in the best attenuation of surgical stimulation disturbances. Partitioning based on age for children and weight for adults reduces the impact from surgical stimulation were proposed for clinical practices. These methods are easy to implement because the demographics are known beforehand and does not depend on actual measurements during the anesthesia. The results are substantiated by simulations and calculations of achieved attenuation with acceptable performance and preserved robustness.