Genetic fuzzy modelling and control of bispectral index (BIS) for general intravenous anaesthesia.
Based on an adaptive genetic fuzzy clustering algorithm, a derived fuzzy knowledge model is proposed for quantitatively estimating the systolic arterial pressure (SAP), heart rate (HR), and bispectral index (BIS) using 12 patients and it validates them according to pharmacological reasoning. Also, a genetic proportional integral derivative controller (GPIDC) to adaptive three controller parameters and a genetic fuzzy logic controller (GFLC) to adaptive controller rules using genetic algorithms (GAs) were simulated and compared each other in a patient model using the BIS value as a controlled variable. Each controller was tested using a set of 12 virtual patients undergoing a Gaussian random surgical disturbance repeated with BIS targets set at 40, 50, and 60. Controller performance was assessed using mean absolute error (MAE) of the BIS target, the percentage of time with acceptable BIS control (PTABC), and drug consumption (DC). It was found that the MAE value of the BIS target was significantly lower (P < 0.05) and the values of PTABC and DC of BIS target were significantly higher (P < 0.05) in BIS targets set at 40 than at 50 or 60 in both GPIDC and GFLC. However, when compared with two controllers in terms of the values of MAE, PTABC, and DC each other in BIS targets set at 40, 50, and 60, there were no significant differences (P > 0.05). Furthermore, when the simulation results in these two controllers were compared with routine standard practice of 12 clinical trials (i.e., manual control) in BIS target set at 50, the values of PTABC in both GPIDC and GFLC groups were significantly higher (P < 0.05) than in the manual control group. In contrast, there were no significant differences (P > 0.05) for these three groups in terms of drug consumption. This indicates that either GPIDC or GFLC can control the BIS target set at 50 better than manual control, although the similar drug consumption is used.
Shieh
Jiann-Shing
JS
Department of Mechanical Engineering, Yuan Ze University, 135 Yuan-Tung Rd., Chung-Li, Taoyuan 320, Taiwan. jsshieh@saturn.yzu.edu.tw
Kao
Ming-Hsien
MH
Liu
Chien-Chiang
CC
D000768:Anesthesia, General / Q000379:methods*; D018686:Anesthetics, Intravenous / Q000008:administration & dosage* / Q000493:pharmacokinetics; D001185:Artificial Intelligence; D001794:Blood Pressure / Q000187:drug effects*; D003198:Computer Simulation; D004360:Drug Therapy, Computer-Assisted / Q000379:methods*; D004562:Electrocardiography / Q000187:drug effects*; D004569:Electroencephalography / Q000187:drug effects*; D005246:Feedback; D005260:Female; D017143:Fuzzy Logic; D006801:Humans; D008297:Male; D008954:Models, Biological; D010363:Pattern Recognition, Automated / Q000379:methods
D018686:Anesthetics, Intravenous