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.

One-Class-Based Intelligent Classifier for Detecting Anomalous Situations During the Anesthetic Process

Abstract Closed-loop administration of propofol for the control of hypnosis in anesthesia has evidenced an outperformance when comparing it with manual administration in terms of drug consumption and post-operative recovery of patients. Unlike other systems, the success of this strategy lies on the availability of a feedback variable capable of quantifying the current hypnotic state of the patient. However, the appearance of anomalies during the anesthetic process may result in inaccurate actions of the automatic controller. These anomalies may come from the monitors, the syringe pumps, the actions of the surgeon or even from alterations in patients. This could produce adverse side effects that can affect the patient postoperative and reduce the safety of the patient in the operating room. Then, the use of anomaly detection techniques plays a significant role to avoid this undesirable situations. This work assesses different one-class intelligent techniques to detect anomalies in patients undergoing general anesthesia. Due to the difficulty of obtaining real data from anomaly situations, artificial outliers are generated to check the performance of each classifier. The final model presents successful performance.

Multi-model generalised predictive control for intravenous anaesthesia under inter-individual variability.

Inter-individual variability possesses a major challenge in the regulation of hypnosis in anesthesia. Understanding the variability towards anesthesia effect is expected to assist the design of controller for anesthesia regulation. However, such studies are still very scarce in the literature. This study aims to analyze the inter-individual variability in propofol pharmacokinetics/pharmacodynamics (PK/PD) model and proposed a suitable controller to tackle the variability. This study employed Sobol' sensitivity analysis to identify significance parameters in propofol PK/PD model that affects the model output Bispectral Index (BIS). Parameters' range is obtained from reported clinical data. Based on the finding, a multi-model generalized predictive controller was proposed to regulate propofol in tackling patient variability. [Formula: see text] (concentration that produces 50% of the maximum effect) was found to have a highly-determining role on the uncertainty of BIS. In addition, the Hill coefficient, [Formula: see text], was found to be significant when there is a drastic input, especially during the induction phase. Both of these parameters only affect the process gain upon model linearization. Therefore, a predictive controller based on switching of model with different process gain is proposed. Simulation result shows that it is able to give a satisfactory performance across a wide population. Both the parameters [Formula: see text] and [Formula: see text], which are unknown before anesthesia procedure, were found to be highly significant in contributing the uncertainty of BIS. Their range of variability must be considered during the design and evaluation of controller. A linear controller may be sufficient to tackle most of the variability since both [Formula: see text] and [Formula: see text] would be translated into process gain upon linearization.

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.

An assessment of the validity of spectral entropy as a measure of sedation statein mechanically ventilated critically ill patients

To assess whether the Entropy Module (GE Healthcare, Helsinki, Finland), a device to measure hypnosis in anesthesia, is a valid measure of sedation state in critically ill patients by comparing clinically assessed sedation state with Spectral Entropy Prospective observational study. Teaching hospital general ICU. 30 intubated, mechanically ventilated patients without primary neurological diagnoses or drug overdose receiving continuous sedation. Monitoring of EEG and fEMG activity via forehead electrodes for up to 72h and assessments of conscious level using a modified Ramsay Sedation Scale. 475 trained observer assessments were made and compared with concurrent Entropy numbers. Median State (SE) and Response (RE) Entropy values decreased as Ramsay score increased, but wide variation occurred, especially in Ramsay 4-6 categories. Discrimination between different sedation scores [mean (SEM) P(K) value: RE 0.713 (0.019); SE 0.710 (0.019)] and between lighter (Ramsay 1-3) vs.deeper (Ramsay 4-6) sedation ranges was inadequate [P(K): RE 0.750 (0.025); SE 0.748 (0.025)]. fEMG power decreased with increasing Ramsay score but was often significant even at Ramsay 4-6 states. Frequent "on-off" effects occurred for both RE and SE, which were associated with fEMG activity. Values switched from low to high values even in deeply sedated patients. High Entropy values during deeper sedation were strongly associated with simultaneous high relative fEMG powers. Entropy of the frontal EEG does not discriminate sedation state adequately for clinical use in ICU patients. Facial EMG is a major confounder in clinical sedation ranges.

Master Class For Practitioners The art of practice informed by theory and the findings of research the use of Hypnosis in Anesthesia: A Master Class Commentary

There are unequivocal benefits derived from the use of positive suggestion and hypnotic techniques in all patients who must submit to surgical and obstetrical procedures with modern general or regional anesthesia. We must learn, and we must teach our colleagues, the advantages of consistent use of the semantics of positive suggestion. When we help patients focus on the desired comfort, safety, and satisfaction obtained with well-managed modern anesthesia and surgery, they will enjoy great benefit, especially when we use the auditory perception that often exists during general anesthesia. Rather than regarding hypnotic suggestion as a mere adjunct to anesthesia, it should be regarded as an integral part of surgical and obstetrical care.

Hypnosis in anesthesia: some psychological considerations.

Hypnosis in anesthesia: some psychological considerations.