An Adaptive Neural Network Filter for Improved Patient State Estimation in Closed-Loop Anesthesia Control

Recent studies in the controlled administration of intravenous propofol favor a robust automated delivery control system in lieu of a manual controller. In previous work, a Reinforcement Learning (RL) controller was successfully tested in silico and in human volunteers with promising results. In this paper, an Adaptive Neural Network Filter (ANNF) is introduced in an effort to improve RL control of propofol hypnosis. The modified controller was tested in silico on simulated intraoperative patients, and its performance was compared against previously published results. Results from the experiments show that the new controller outperformed the previous controller in the maintenance of propofol anesthesia, with modest improvement in performance during anesthetic induction.