title: Reinforcement learning versus proportional-integral-derivative control of hypnosis in a simulated intraoperative patient.
abstract: BACKGROUND
  Research has demonstrated the efficacy of closed-loop control of anesthesia using bispectral index (BIS) as the controlled variable. Model-based and proportional-integral-derivative (PID) controllers outperform manual control. We investigated the application of reinforcement learning (RL), an intelligent systems control method, to closed-loop BIS-guided, propofol-induced hypnosis in simulated intraoperative patients. We also compared the performance of the RL agent against that of a conventional PID controller.
  
  
  METHODS
  The RL and PID controllers were evaluated during propofol induction and maintenance of hypnosis. The patient-hypnotic episodes were designed to challenge both controllers with varying degrees of interindividual variation and noxious surgical stimulation. Each controller was tested in 1000 simulated patients, and control performance was assessed by calculating the median performance error (MDPE), median absolute performance error (MDAPE), Wobble, and Divergence for each controller group. A separate analysis was performed for the induction and maintenance phases of hypnosis.
  
  
  RESULTS
  During maintenance, RL control demonstrated an MDPE of -1% and an MDAPE of 3.75%, with 80% of the time at BIS(target) ± 5. The PID controller yielded a MDPE of -8.5% and an MDAPE of 8.6%, with 57% of the time at BIS(target) ± 5. In comparison, the MDAPE in the worst-controlled patient of the RL group was observed to be almost half that of the worst-controlled patient in the PID group.
  
  
  CONCLUSIONS
  When compared with the PID controller, RL control resulted in slower induction but less overshoot and faster attainment of steady state. No difference in interindividual patient variation and noxious destabilizing challenge on control performance was observed between the 2 patient groups.
authors.affiliation: Department of Computer Science, Texas Tech University, Lubbock, Texas, USA.
authors.forename: Brett L
authors.initials: BL
authors.lastname: Moore
authors.forename: Todd M
authors.initials: TM
authors.lastname: Quasny
authors.forename: Anthony G
authors.initials: AG
authors.lastname: Doufas
mesh_terms: D000764:Anesthesia, Closed-Circuit; D018686:Anesthetics, Intravenous / Q000008:administration & dosage* / Q000493:pharmacokinetics; D001185:Artificial Intelligence; D003198:Computer Simulation; D056805:Consciousness Monitors; D004305:Dose-Response Relationship, Drug; D006801:Humans; D006991:Hypnosis, Anesthetic; D007432:Intraoperative Period; D008297:Male; D008962:Models, Theoretical; D016343:Monitoring, Intraoperative / Q000295:instrumentation / Q000379:methods; D016544:Patient Simulation; D010363:Pattern Recognition, Automated; D015742:Propofol / Q000008:administration & dosage* / Q000493:pharmacokinetics; D012815:Signal Processing, Computer-Assisted; D055815:Young Adult
chemical_list: D018686:Anesthetics, Intravenous; D015742:Propofol