Regulation of hypnosis in Propofol anesthesia administration based on non-linear control strategy

Abstract

Continuous adjustment of Propofol in manual delivery of anesthesia for conducting a surgical procedure overburdens the workload of an anesthetist who is working in a multi-tasking scenario. Going beyond manual administration and Target Controlled Infusion, closed-loop control of Propofol infusion has the potential to offer several benefits in terms of handling perturbations and reducing the effect of inter-patient variability. This paper proposes a closed-loop automated drug administration approach to control Depth Of Hypnosis in anesthesia. In contrast with most of the existing research on anesthesia control which makes use of linear control strategies or their improved variants, the novelty of the present research lies in applying robust control strategy i.e. Sliding Mode Control to accurately control drug infusion. Based on the derived patient's model, the designed controller uses measurements from EEG to regulate DOH on Bispectral Index by controlling infusion rate of Propofol. The performance of the controller is investigated and characterized with real dataset of 8 patients undergoing surgery. Results of this in silico study indicate that for all the patients, with 0% overshoot observed, the steady state error lies in between ±5. Clinically, this implies that in all the cases, without any overdose, the controller maintains the desired DOH level for smooth conduction of surgical procedures.

AnesthesiaGUIDE: a MATLAB tool to control the anesthesia

“…The ranges between 60-70 and 40-60 represent high and moderate hypnotic conditions, respectively. During the surgery, the target value for the BIS is 50 and it is recommended to be kept between 40 and 60 [6][7][8][9].…”

“…In the literature, many closed-loop anesthesia systems are proposed. Some relevant examples may be mentioned here: Ilyas proposed a sliding-mode control algorithm [6] [7], Khaqan developed a nonlinear strategy [8], Navarro designed a fractional-order closed-loop model reference adaptive control based on a fractional-order model [9], Mendez used fuzzy predictive control techniques [10], Caiado investigated H-inf robust control [11], Reznanian developed a predictive controller [12], Usharani developed a PID controller strategy [16], Pasin performed a metaanalysis comparing closed-loop with manual drug delivery [17], Sopasakis built a fractional pharmacokinetics model https://doi.org/10.1007/s42452-021-04885-x [18], and Gonzalez-Cava proposed a study where an adaptive model deal simultaneously with the variabilities in the clinical response of the patients [19]. Actual trends and challenges are presented by Ghita in [20] and Zaouter in [21].…”

The development of healthcare mobile robot for helping medical personnel in dealing with COVID-19 patients

“…There are several previous studies regarding healthcare robotics. As implemented in the Polyclinic of Modena (Italy) [6], Humanoid companion robot for patients [7], assistive robot [8]- [11], end-effector wheeled robotic arm [12], automation and control system in medical sector [13]- [15], and also a robot with a rehabilitation function and motion assitance [16]- [23]. In addition, there are also robots with slightly different goals, namely to disinfect the environment using UV light which can also be applied for healthcare purposes [24].…”

Robotics Inspired Renewable Energy Developments: Prospective Opportunities and Challenges

“…Robotics engineering is now being acknowledged as a dedicated branch of engineering [6]. Robots are extensively used in agriculture [7], food [8], [9], medical [10]- [14], cognition [15]- [18], nuclear [19], space [20]- [22], aerospace [23]- [25], under-water [26], [27], industrial [28], [29], oil and gas [30], textile [31] and in other tracking [32], [33] applications. International Federation of Robotics (IFR) is considered as a primary resource of providing data on the worldwide use of robots.…”