An artificial neural network-based controller for the control of induced paralysis using vecuronium bromide.
This study presents an artificial neural network-based controller for regulating the level of induced paralysis during surgery using vecuronium bromide. The controller uses the myogram of a rapid muscle contractions (called twitch) to generate the appropriate infusion rate. The controller is self-adjusting and can accommodate inter- and intrapatient drug response variations. It also withstands changes in the pure time delay and nonlinear pharmacokinetic parameters of the response. Another feature of the controller is that it does not depend on a priori knowledge of the patient response model. Computer simulations using pharmacokinetic and pharmacodynamic models showed negligible steady-state error and maximum percent undershoot averaged to 6.24%. The average infusion rate for 90% paralysis was 1.22 (microg x kg(-1) x min[-1]).
Kamangar
F A
FA
Computer Science and Engineering, University of Texas at Arlington, 76019, USA.
Behbehani
K
K
D003198:Computer Simulation; D004867:Equipment Design; D006801:Humans; D007260:Infusion Pumps; D007262:Infusions, Intravenous; D007432:Intraoperative Period; D008954:Models, Biological; D016571:Neural Networks, Computer; D019148:Neuromuscular Blockade; D009467:Neuromuscular Depolarizing Agents / Q000008:administration & dosage* / Q000493:pharmacokinetics; D017711:Nonlinear Dynamics; D010243:Paralysis / Q000139:chemically induced; D013813:Therapy, Computer-Assisted; D013997:Time Factors; D014673:Vecuronium Bromide / Q000008:administration & dosage* / Q000493:pharmacokinetics
D009467:Neuromuscular Depolarizing Agents; D014673:Vecuronium Bromide