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.
KeywordsFuzzy Logic Mean Arterial Pressure Fuzzy Controller Adaptive Network Base Fuzzy Inference System Consequent Parameter
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- 3.Zhang X.-S. and Roy RJ, “Depth of anesthesia estimation by adaptivenetwork-based fuzzy inference system”, Proceedings of The First Joint BMES/EMBS Conference, Atlanta, Georgia, p.391, Oct. 1999Google Scholar
- 5.Abbod MF and Linkens DA, “Anaesthesia monitoring and control using fuzzy logic fusion”, Biomedical Engineering Application, Basis Communications, vol. 10, no.4, pp.225–235, 1998.Google Scholar
- 10.Huang JW, Held CM, and Roy RJ, “Hemodynamic management with multiple drugs using fuzzy logic”, in (Teodorescu H-N, Kandel A, and Jain LC, eds): Fuzzy and neuro-fuzzy systems in medicine, CRC Press (Boca Raton, London, New York, and Washington DC), chapter 11(pp.319–340), 1999.Google Scholar
- 16.Abramovitch DY and Bushnell LG, “Report on the fuzzy versus conventional control debate”, IEEE Control Systems, pp.88–91, June, 1999.Google Scholar
- 18.Zhang X-S, Roy RJ, Schwender D, and Daunderer M, “Discrimination of anesthetic states using midlatency auditory evoked potentials and artificial neural networks”, Anesth. Analg. (under review).Google Scholar
- 28.Rao R, Bequette WB, Huang JW, Roy RJ, Kaufman H, “Modeling and Control of Anesthetic and Hemodynamic Drug Infusion”, AIChE 1997 Fall Meeting, LA — Session 08b12.Google Scholar
- 44.Zhang, X-S, Roy RJ, and Huang JW, “Closed-loop system for total intravenous anesthesia by simultaneously administering two anesthetic drugs”, Proc. of 20th Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society, Hong Kong, 20:3052–3055, 1998.Google Scholar
- 45.Shafer S, STANPUMP software, Stanford University Medical Center, http://pkpd.icon.palo-alto.med.va.gov.Google Scholar
- 46.Cox E, “Adaptive fuzzy systems”, IEEE Spectrum, pp. 27–31, Feb. 1993.Google Scholar
- 47.Kang H and Vachtsevanos G, “Adaptive fuzzy logic control”, Proc. of the IEEE Int. Conf. on Fuzzy Systems1992, San Diego, Mar. 1992; 407–14.Google Scholar