Depth Of Hypnosis In Anesthesia Research Articles

Performance Evaluation of ILC Controller for Anesthesia Process

An adequate adjust of propofol is crucial to maintain the depth of hypnosis in anesthesia. It is a tedious task of anesthesiologist during surgery. The less amount of propofol infusion leads to insufficient anesthesia and higher amount stands dangerous for the patient. Here, iterative learning controller (ILC) is designed to control depth of hypnosis in anesthesia with BIS as feedback signal. BIS is calculated from EEG signal. In this, automatic propofol infusion system, the patient model, is developed based on pharmacokinetic and pharmocodynamic (PK/PD) model in MATLAB/SIMULINK. The anesthesia process is approximated to transfer function model of first-order time delay. The conventional Internal Model Control-based PID controller (IMC PID) and Ziegler Nichols PID (ZN PID) controller are used to compare the efficiency of proposed ILC controller. The performance results in terms of quality indices (overshoot, settling time) and error indices (Integral Square Error (ISE) and Integral Absolute Error (IAE)) show that the ILC controller is superior than conventional controllers.

Optimized PID control of propofol and remifentanil coadministration for general anesthesia

A closed-loop control system for the control of the depth of hypnosis in anesthesia by using propofol-remifentanil coadministration and the Bispectral Index as feedback signal is proposed. A PID controller is employed together with a fixed ratio between propofol and remifentanil infusions. The ratio allows the anesthesiologist to control the opioid-hypnotic balance during surgery. Optimal tuning rules for the proposed controller are obtained off-line by using the particle swarm optimization method and by considering a given dataset of patients and a wide range of infusion ratios that covers a rich set of scenarios that might occur in clinical practice. Finally, a gain scheduling strategy guarantees the optimality of the performance both for the induction and the maintenance phase. A Monte Carlo approach is used to evaluate the robustness of the method with respect to intra- and inter-patient variability.

A model-based control scheme for depth of hypnosis in anesthesia

Abstract In this paper we propose a model-based scheme to control the depth of hypnosis in anesthesia that uses the BIS signal as controlled variable. In particular, the control scheme exploits the propofol pharmacokinetics/pharmacodynamics model of the patient so that the estimated effect-site concentration is used as a feedback signal for a standard PID controller, which compensates for the model uncertainties. The tuning of the parameters is performed off-line using genetic algorithms to minimize a performance index over a given data set of patients. The effectiveness of the proposed method is verified by means of a Monte Carlo method that takes into account both the intra-patient and inter-patient variability. In general, we obtain a fast induction phase with limited overshoot and a good disturbance rejection during maintenance of anesthesia.

Two-degree-of-freedom control scheme for depth of hypnosis in anesthesia

In this work, a Proportional-Integral-Derivative (PID) controller based two-degree-of-freedom control scheme for Depth of Hypnosis (DoH) in general anesthesia is proposed. This approach uses the Bispectral Index Scale (BIS) as a controlled variable and propofol administration as a control variable. The developed structure applies a new compensation scheme, which reduces the influence of the nonlinear element. In this context, we exploit the linear part of the patient model, that can be obtained from the demographics of each individual patient. The parameters are tuned using the optimization procedure based on a genetic algorithm. The evaluation of the proposed technique is performed using intra-patient variability with a Monte Carlo method. Additionally, the performance of the analyzed system has been verified using several indexes. The simulation results show that desired characteristics are obtained for both induction and maintenance phases.

Event-Based control of depth of hypnosis in anesthesia

Background and Objective: In this paper, we propose the use of an event-based control strategy for the closed-loop control of the depth of hypnosis in anesthesia by using propofol administration and the bispectral index as a controlled variable.Methods: A new event generator with high noise-filtering properties is employed in addition to a PIDPlus controller. The tuning of the parameters is performed off-line by using genetic algorithms by considering a given data set of patients.Results: The effectiveness and robustness of the method is verified in simulation by implementing a Monte Carlo method to address the intra-patient and inter-patient variability. A comparison with a standard PID control structure shows that the event-based control system achieves a reduction of the total variation of the manipulated variable of 93% in the induction phase and of 95% in the maintenance phase.Conclusions: The use of event based automatic control in anesthesia yields a fast induction phase with bounded overshoot and an acceptable disturbance rejection. A comparison with a standard PID control structure shows that the technique effectively mimics the behavior of the anesthesiologist by providing a significant decrement of the total variation of the manipulated variable.

Optimized PID control of depth of hypnosis in anesthesia

Background and Objective: This paper addresses the use of proportional-integral-derivative controllers for regulating the depth of hypnosis in anesthesia by using propofol administration and the bispectral index as a controlled variable. In fact, introducing an automatic control system might provide significant benefits for the patient in reducing the risk for under- and over-dosing.Methods: In this study, the controller parameters are obtained through genetic algorithms by solving a min-max optimization problem. A set of 12 patient models representative of a large population variance is used to test controller robustness. The worst-case performance in the considered population is minimized considering two different scenarios: the induction case and the maintenance case.Results: Our results indicate that including a gain scheduling strategy enables optimal performance for induction and maintenance phases, separately. Using a single tuning to address both tasks may results in a loss of performance up to 102% in the induction phase and up to 31% in the maintenance phase. Further on, it is shown that a suitably designed low-pass filter on the controller output can handle the trade-off between the performance and the noise effect in the control variable.Conclusions: Optimally tuned PID controllers provide a fast induction time with an acceptable overshoot and a satisfactory disturbance rejection performance during maintenance. These features make them a very good tool for comparison when other control algorithms are developed.

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

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.

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

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.