Depth of Anesthesia Control with Fuzzy Logic

The anesthetic management of a surgical patient is a process that relies on the experience of an anesthesiologist, since currently there is no direct means of assessing a patient’s level of consciousness during surgery. The decision for the initial anesthetic level is generally made by using the recommended drug dosages based on various patient characteristics, such as age and weight. The anesthesiologist determines any subsequent alteration in the anesthetic level by observing signs from the patient. These signs, the indirect indicators of the depth of anesthesia (DOA), may include changes in blood pressures or heart rate, lacrimation, facial grimacing, muscular movement, spontaneous breathing, diaphoresis, and other signs that may predicate awareness. However, they are not reliable indicators of changes in a patient’s level of consciousness. Although an anesthesiologist can adjust recommended anesthetic dosages based on individual patient characteristics, these adjustments cannot always account for variability in patient responses to anesthesia or changes in anesthetic requirements during the course of surgery.

[1]  Propofol concentration required for endotracheal intubation with a laryngoscope or fiberscope and its interaction with fentanyl , 1998 .

[2]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[3]  D. A. Linkens,et al.  Anaesthesia monitoring and control using fuzzy logic fusion , 1998 .

[4]  Abraham Lempel,et al.  On the Complexity of Finite Sequences , 1976, IEEE Trans. Inf. Theory.

[5]  R. Bagshaw,et al.  Possible interaction of esmolol and nitrous oxide. , 1997, Anesthesiology.

[6]  R.J. Roy,et al.  Depth of anesthesia estimation and control [using auditory evoked potentials] , 1999, IEEE Transactions on Biomedical Engineering.

[7]  E Olofsen,et al.  Propofol Anesthesia and Rational Opioid Selection: Determination of Optimal EC50‐EC95 Propofol‐Opioid Concentrations that Assure Adequate Anesthesia and a Rapid Return of Consciousness , 1997, Anesthesiology.

[8]  G. Wanner,et al.  Effect of Intravenous Anesthetics on Spontaneous and Endotoxin‐stimulated Cytokine Response in Cultured Human Whole Blood , 1998, Anesthesiology.

[9]  T. Kazama,et al.  Awakening Propofol Concentration with and without Blood‐Effect Site Equilibration after Short‐term and Long‐term Administration of Propofol and Fentanyl Anesthesia , 1998, Anesthesiology.

[10]  A. Burm,et al.  Performance of Computer-Controlled Infusion of Propofol: An Evaluation of Five Pharmacokinetic Parameter Sets , 1995, Anesthesia and analgesia.

[11]  D R Stanski,et al.  Pharmacodynamic modeling of anesthetic EEG drug effects. , 1992, Annual review of pharmacology and toxicology.

[12]  S. Ranta,et al.  Awareness with Recall During General Anesthesia: Incidence and Risk Factors , 1998, Anesthesia and analgesia.

[13]  J.W. Huang,et al.  Multiple-drug hemodynamic control using fuzzy decision theory , 1998, IEEE Transactions on Biomedical Engineering.

[14]  R. Roy,et al.  A461 CLOSED LOOP INTRAVENOUS ANESTHETIC ADMINISTRATION , 1997 .

[15]  J. Röschke,et al.  Discrimination of sleep stages: a comparison between spectral and nonlinear EEG measures. , 1996, Electroencephalography and clinical neurophysiology.

[16]  R.J. Roy,et al.  Multiple drug hemodynamic control by means of a supervisory-fuzzy rule-based adaptive control system: validation on a model , 1995, IEEE Transactions on Biomedical Engineering.

[17]  Ebrahim H. Mamdani,et al.  An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller , 1999, Int. J. Hum. Comput. Stud..

[18]  G. Vachtsevanos,et al.  Adaptive fuzzy logic control , 1992, [1992 Proceedings] IEEE International Conference on Fuzzy Systems.

[19]  J. Glen,et al.  Pharmacokinetic Model Selection for Target Controlled Infusions of Propofol: Assessment of Three Parameter Sets , 1994, Anesthesiology.

[20]  Xu-Sheng Zhang,et al.  Discrimination of anesthetic states using midlatency auditory evoked potentials and artificial neural networks , 2000, Proceedings of the 22nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (Cat. No.00CH37143).

[21]  N. T. Smith,et al.  Improved safety and efficacy in adaptive control of arterial blood pressure through the use of a supervisor , 1992, IEEE Transactions on Biomedical Engineering.

[22]  James C. Scott,et al.  Electroencephalographic quantitation of opioid effect: comparative pharmacodynamics of fentanyl and sufentanil. , 1991, Anesthesiology.

[23]  J G Bovill,et al.  The Pharmacodynamic Interaction of Propofol and Alfentanil during Lower Abdominal Surgery in Women , 1995, Anesthesiology.

[24]  A J Asbury,et al.  Fuzzy logic: new ways of thinking for anaesthesia. , 1995, British journal of anaesthesia.

[25]  M. Sugeno,et al.  Structure identification of fuzzy model , 1988 .

[26]  T. Kazama,et al.  The Pharmacodynamic Interaction between Propofol and Fentanyl with Respect to the Suppression of Somatic or Hemodynamic Responses to Skin Incision, Peritoneum Incision, and Abdominal Wall Retraction , 1998, Anesthesiology.

[27]  E. Eger,et al.  Minimum alveolar anesthetic concentration: a standard of anesthetic potency. , 1965, Anesthesiology.

[28]  M. Todd,et al.  EEGs, EEG processing, and the bispectral index. , 1998, Anesthesiology.

[29]  Derek A. Linkens,et al.  Fuzzy logic for auditory evoked response monitoring and control of depth of anaesthesia , 1998, Fuzzy Sets Syst..

[30]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[31]  E. Olofsen,et al.  Pharmacodynamic Interaction between Propofol and Alfentanil When Given for Induction of Anesthesia , 1996, Anesthesiology.

[32]  L.G. Bushnell,et al.  Report on the fuzzy versus conventional control debate , 1999, IEEE Control Systems.

[33]  T. Kazama,et al.  Reduction by Fentanyl of the Cp50 Values of Propofol and Hemodynamic Responses to Various Noxious Stimuli , 1997, Anesthesiology.

[34]  Kazuyuki Ikeda,et al.  The Effects of Fentanyl on Sevoflurane Requirements for Loss of Consciousness and Skin Incision , 1998, Anesthesiology.

[35]  A. McEwan,et al.  The Interaction of Fentanyl on the Cp50 of Propofol for Loss of Consciousness and Skin Incision , 1994, Anesthesiology.

[36]  P. Sebel,et al.  Propofol: A New Intravenous Anesthetic , 1989, Anesthesiology.

[37]  J. Glen,et al.  Pharmacokinetic model selection for target controlled infusions of propofol. Assessment of three parameter sets. , 1994 .

[38]  E. Cox.  Adaptive fuzzy systems , 1993, IEEE Spectrum.

[39]  Xu-Sheng Zhang,et al.  Depth of anesthesia estimation by adaptive-network-based fuzzy inference system , 1999, Proceedings of the First Joint BMES/EMBS Conference. 1999 IEEE Engineering in Medicine and Biology 21st Annual Conference and the 1999 Annual Fall Meeting of the Biomedical Engineering Society (Cat. N.

[40]  Udilo Finsterer,et al.  Midlatency Auditory Evoked Potentials Predict Movements During Anesthesia with Isoflurane or Propofol , 1997, Anesthesia and analgesia.

[41]  A. Nayak,et al.  Anesthesia control using midlatency auditory evoked potentials , 1998, IEEE Transactions on Biomedical Engineering.

[42]  J T Harvey,et al.  Computer controlled infusion of propofol. , 1989, British journal of anaesthesia.

[43]  Jaap Vuyk,et al.  Pharmacodynamics of Alfentanil as a Supplement to Propofol or Nitrous Oxide for Lower Abdominal Surgery in Female Patients , 1993, Anesthesiology.

[44]  J. W. Huang,et al.  Closed-loop system for total intravenous anesthesia by simultaneously administering two anesthetic drugs , 1998, Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Vol.20 Biomedical Engineering Towards the Year 2000 and Beyond (Cat. No.98CH36286).