Adpative Neuro-Fuzzy Inference System Estimation Propofol dose in the induction phase during anesthesia; case study

Abstract

In this study, the anesthetic drug dose estimation due to the physiological patients' parameters is considered. The most critical anesthetic drug, propofol, is considered in this modeling. Among the intravenous anesthetic drugs, propofol is one of the most widely used during surgery in the induction and maintenance phase of anesthesia. The effect of propofol as an intravenous anesthetic agent is as well as sedate in/outside the operation theatres. In this work, the adaptive neuro-fuzzy inference system estimation model is applied to calculate the drug dose to administrate anesthesia safety. The model estimates the propofol dose during the induction phase based on the physiological parameters (age, weight, height, gender), blood pressure, heart rate, and the depth of anesthesia of real patients. The sensitivity analysis was applied to evaluate the validity of the estimation model, so the appropriate agreement is obtained. In the end, the proposed estimation model's performance is compared to the classical model and the actual data obtained from patients undergoing surgery. The results show that the ANFIS estimation model by 0.999 accuracies reduces the total amount of propofol dose. The proposed model not only controls the patient's depth of anesthesia accurately but also obtained outcomes in practice successfully.