Optimized PID control of depth of hypnosis in anesthesia. (June 2017)

• Record Type: Journal Article
• Authors: Padula, Fabrizio; Ionescu, Clara; Latronico, Nicola; Paltenghi, Massimiliano; Visioli, Antonio; Vivacqua, Giulio
• Is Part Of: Computer methods and programs in biomedicine. Volume 144(2017)
• Page Start: 21
• Page End: 35
• Publication Date: 2017-06
• Subjects: Depth of hypnosis control -- PID control -- Gain scheduling -- Genetic algorithms; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2017.03.013
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: This paper deals with the use of proportional-integral-derivative controllers for the closed-loop control of the depth of hypnosis in anesthesia by using propofol administration and the bispectral index as a controlled variable. The controller parameters are optimized by using genetic algorithms and it is shown that a gain scheduling strategy should be employed to address the induction and maintenance phases separately. The selection of the filter on the controller output is also considered and the trade-off between the performance and the noise effect in the control variable is analyzed. Abstract: Background and Objective: This paper addresses the use of proportional-integral-derivative controllers for regulating the depth of hypnosis in anesthesia by using propofol administration and the bispectral index as a controlled variable. In fact, introducing an automatic control system might provide significant benefits for the patient in reducing the risk for under- and over-dosing. Methods: In this study, the controller parameters are obtained through genetic algorithms by solving a min-max optimization problem. A set of 12 patient models representative of a large population variance is used to test controller robustness. The worst-case performance in the considered population is minimized considering two different scenarios: the induction case and the maintenance case. Results: Our results indicate that including a gain scheduling strategy enables optimal performance for induction and maintenance phases, separately. Using a single tuning to address both tasks may results in a loss of performance up to 102% in the induction phase and up to 31% in the maintenance phase. Further on, it is shown that a suitably designed low-pass filter on the controller output can handle the trade-off between the performance and the noise effect in the control variable. Conclusions: Optimally tuned PID controllers provide a fast induction time with an acceptable overshoot and a satisfactory disturbance rejection performance during maintenance. These features make them a very good tool for comparison when other control algorithms are developed.