Fractional calculus has been opening new doors in terms of better modeling and control of several phenomena and processes. Biomedical engineering has seen a lot of combined attention from clinicians, control engineers and researchers in their attempt to offer individualized treatment. A large number of medical procedures require anesthesia, which in turn requires a closely monitored and controlled level of hypnosis, analgesia and neuromuscular blockade, as well maintenance of hemodynamic variables in a safe range. Computer-controlled anesthesia has been given a tremendous amount of attention lately. Hemodynamic stabilization via computer-based control is also a hot topic. However, very few studies on automatic control of combined anesthesia–hemodynamic systems exist despite the fact that hemodynamics is strongly influenced by hypnotic drugs, while the depth of hypnosis is affected by drugs used in hemodynamic control. The very first multivariable fractional-order controller is developed in this paper for the combined anesthesia–hemodynamic system. Simulation studies on 24 patients show the effectiveness of the proposed approach.

Within biomedical engineering, there has been significant collaboration among clinicians, control engineers, and researchers to tailor treatments to individual patients. Anesthesia is integral to numerous medical procedures, necessitating precise management of hypnosis, analgesia, neuromuscular blockade, and hemodynamic variables. Recent attention has focused on computer-controlled anesthesia and hemodynamic stabilization. This research proposes the integration of a decentralized control strategy for the induction phase with a decoupled control approach for the maintenance phase, aimed at mitigating interactions within the multivariable human system. The proposed strategy is based on fractional-order controllers. The solution is validated using an open-source patient simulator featuring data from 24 virtual patients, demonstrating the efficiency of the proposed approach with respect to decentralized control.

The integration of information technology and control engineering has significantly increased in clinical practice research, especially in the management of drug dosing for general anaesthesia. The main focus of the research is to achieve a personalised control of the Depth of Hypnosis based on a novel approach. The primary objective is to regulate the Bispectral Index by administering Propofol during the hypnotic phase of anaesthesia. A fractional order time delay model is proposed to replace the conventional PK-PD model. This model is estimated online during the induction phase. A fractional order Proportional-Integral-Derivative (PID) controller is then individually tuned for each patient. The closed loop simulation results demonstrate that the proposed controllers effectively minimise undershoot and achieve a short time to reach the desired time-to-target for the majority of the patients. The analysis indicates that the proposed controller is capable of maintaining the BIS signal within a safe range. Comparative analyses with similar research are provided to effectively contextualise this study within the existing literature. Disturbance rejection tests during the maintenance phase are performed to demonstrate the effectiveness of personalised control. The online adaptation that this research brings, compared to the previous attempts, allows for significantly improved results for the hypnosis phase.

The research field of clinical practice has experienced a substantial increase in the integration of information technology and control engineering, which includes the management of medication administration for general anaesthesia. The invasive nature of input signals is the reason why autotuning methods are not widely used in this research field. This study proposes a non-invasive method using small-amplitude sine tests to estimate patient parameters, which allows the design of a personalised controller using an autotuning principle. The primary objective is to regulate the Bispectral Index through the administration of Propofol during the maintenance phase of anaesthesia, using a personalised fractional-order PID. This work aims to demonstrate the effectiveness of personalised control, which is facilitated by the proposed sine-based method. The closed-loop simulation results demonstrate the efficiency of the proposed approach.

Little research has been carried out in terms of modeling and control of analgesia. However, emerging new technology and recent prototypes paved the way for several ideas on pain modeling for control. Recently, such an idea has been proposed for measuring the Depth of Analgesia (DoA). In this paper, that solution is further exploited towards obtaining a novel fractional-order model and dedicated controller for DoA. First, clinical data from patients undergoing general anesthesia are used to determine a commensurate fractional-order model of the skin impedance at each sampling period. Second, we provide a proof of concept indicating that fractional order changes due to variations in the infused opioid drug (Remifentanil). Third, a fractional-order model for DoA is developed correlating the changes in the pain index (as the output signal) and the Remifentanil infusion rate (as the input signal). Standard optimization routines are used to estimate the parameters. A database of 19 real patients is used. Lastly, a preliminary fractional-order controller is designed and tested in simulation for the 19 patients. The closed-loop simulation results correspond to the expected clinical outcomes.