Application of fuzzy learning in IoT-enabled remote healthcare monitoring and control of anesthetic depth during surgery

Smart remote patient monitoring and early disease diagnosis systems have made huge progresses after the introduction of Internet of Things (IoT) and Artificial Intelligence (AI) concepts. This paper proposes an AI-enabled IoT system to monitor and adjust the depth of anesthesia via network channels. More precisely, fuzzy learning systems are employed to develop a control system for the depth of anesthesia in surgeries. This scheme is composed of variable structure control and adaptive type-II fuzzy systems. Therefore, the controller is adaptive and robust to any perturbations and disturbances that may happen during a patient's surgery. The adaptive type-II fuzzy system is designed as an intelligent online estimator to approximate patient model uncertainties. This estimation helps in boosting the performance of the variable structure control system. An artificial neuron is also designed to reduce chattering for the proposed control system. The designed control system can efficiently adjust the anesthesia drug infusion rate and regulate the Bispectral index. The networked structure of the proposed system makes remote tuning of drug infusion possible. Performance of the designed controller is evaluated on several patient models. Simulation results confirm the validity and effectiveness of the proposed remote drug delivery system.