SELF-LEARNING FUZZY CONTROL OF ATRACURIUM-INDUCED NEUROMUSCULAR BLOCKDURING SURGERY

Self-learning fuzzy logic control has the important property of accomm odating uncertain, non-linear and time-varying process characteristics . This intelligent control scheme starts with no fuzzy control rules a nd learns how to control each process presented to it in real time, wi thout the need for detailed process modelling. A suitable medical appl ication to investigate this control strategy is atracurium-induced neu romuscular block (NMB) of patients in the operating theatre. Here, the patient response exhibits high non-linearity, and individual patient dose requirements can vary five-fold during an operating procedure. A portable control system was developed to assess the clinical performan ce of a simplified self-learning fuzzy controller in this application. A Paragraph (Vital Signs) NMB device monitored T-1, the height of the first twitch in a train-of-four nerve stimulation mode. Using a T-1 s etpoint = 10% of baseline in ten patients undergoing general surgery, a mean T-1 error of 0.45% (SD = 0.44%) is found while a 0.13-0.70 mg k (-1) h(-1) range in the mean atracurium infusion rate is accommodated. The result compares favourably with more complex and computationally- intensive model-based control strategies for the infusion of atracuriu m.