Fuzzy Control of Arterial Blood Pressure by Volatile Anesthetics

  • A. M. Zbinden
  • M. Derighetti
  • S. Petersen
  • P. Feigenwinter
Conference paper


While feedback control has found wide application in many areas of modern civilization (such as the regulation of the speed of rapid trains, cars, and cameras), medicine is still — with a few exceptions — exempt. For many controlled parameters during anesthesia automatic feedback control can be applied because the input and output values are well defined and can be easily measured. Anesthetists have to perform many tasks simultaneously; if they can be released from some of the repetitive tasks they can devote more attention to the patient. Automatic feedback systems can help not only to save manpower but also potentially expensive anesthetic gases by minimizing fresh-gas flow. Feedback systems can be used for many tasks, such as:
  • Adjusting tidal volume and frequency depending on the measured expired CO2 concentration and/or the mean airway pressure (or other parameters used to estimate optimization of ventilation)

  • Adjusting the fresh-gas concentration of oxygen nitrous oxide, and/or volatile anesthetics, so that the desired inspired and/or end-tidal concentration is obtained

  • Adjusting the inspired and/or end-tidal concentration of volatile anesthetics, so that a desired mean arterial blood pressure is obtained

  • Minimizing fresh-gas flow of oxygen and nitrous oxide, so that costs for anesthetic gases are reduced while at the same time the ability to adjust rapidly the different gas concentrations is maintained


Fuzzy Control Fuzzy Controller Mean Arterial Blood Pressure Volatile Anesthetic Fuzzy Logic Control 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Sheppard LC (1980) Computer control of the infusion of vasoactive drugs. Ann Biomed Eng 8: 431–444PubMedCrossRefGoogle Scholar
  2. 2.
    Vishnoi R, Roy FJ (1991) Adaptive control of closed circuit anesthesia. IEEE Trans Biomed Eng 38: 39–46PubMedCrossRefGoogle Scholar
  3. 3.
    Rametti LB, Bradlow HS, Uys PC (1985) On-line parameter estimation and control of d-tubocurarine induced muscle relaxation. Med Biol Eng Comput 23: 556–564PubMedCrossRefGoogle Scholar
  4. 4.
    Ashman MN, Blesser WB, Epstein RM (1970) A nonlinear model for the uptake and distribution of halothane in man. Anesthesiology 33: 419–429PubMedCrossRefGoogle Scholar
  5. 5.
    Petersen S, Zbinden AM, Fischer M, Thomson DA (1993) Isoflurane MAC decreases during anesthesia and surgery. Anesthesiology 79: 959–965CrossRefGoogle Scholar
  6. 6.
    Zadeh LA (1965) Fuzzysets. Information Control 8: 338–352CrossRefGoogle Scholar
  7. 7.
    Meier R, Nieuwland J, Zbinden AM, Hacisalihzade SS (1992) Fuzzy logic control of blood pressure during anesthesia. IEEE Control Systems Magazine 12: 12–17CrossRefGoogle Scholar
  8. 8.
    Zbinden AM, Petersen-Felix S, Thomson DA (1994) Defining anesthetic depth using multiple noxious stimuli during isoflurane/oxygen anesthesia. Part 2. Hemodynamic responses. Anesthesiology 80: 261–267Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1995

Authors and Affiliations

  • A. M. Zbinden
  • M. Derighetti
  • S. Petersen
  • P. Feigenwinter

There are no affiliations available

Personalised recommendations