Advanced Control Strategies for the Regulation of Hypnosis with Propofol

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.

Adaptive fuzzy modeling of the hypnotic process in anesthesia

This paper addresses the problem of patient model synthesis in anesthesia. Recent advanced drug infusion mechanisms use a patient model to establish the proper drug dose. However, due to the inherent complexity and variability of the patient dynamics, difficulty obtaining a good model is high. In this paper, a method based on fuzzy logic and genetic algorithms is proposed as an alternative to standard compartmental models. The model uses a Mamdani type fuzzy inference system developed in a two-step procedure. First, an offline model is obtained using information from real patients. Then, an adaptive strategy that uses genetic algorithms is implemented. The validation of the modeling technique was done using real data obtained from real patients in the operating room. Results show that the proposed method based on artificial intelligence appears to be an improved alternative to existing compartmental methodologies.

Data-driven modeling of pharmacological systems using endpoint information fusion

This study investigated the feasibility of deriving data-driven model of a class of pharmacological systems using the information fusion of endpoint responses. For a class of pharmacological systems subsuming conventional steady-state dose-response models, compartmental pharmacokinetic-pharmacodynamic models and indirect response models, a relation between multiple endpoint responses was formalized and analyzed to elucidate if this class of systems is identifiable, i.e., if the data-driven model of this class of systems can be derived from the endpoint responses alone. It was shown that this class of systems is fully identifiable in case all the responses involve effect compartments. However, it was also observed that persistently exciting dose profiles may be required in accurately deriving reliable data-driven model with low variance. The findings from the identifiability analysis were demonstrated using benchmark pharmacological system examples.

Lessons learned from closed loops in engineering: towards a multivariable approach regulating depth of anaesthesia

In this paper is presented a brief state of art regarding the multivariable formulation for controlling the depth of anaesthesia by means of two intravenously administrated drugs, i.e. propofol and remifentanil. In a feasibility study of determining a suitable variable to quantify analgesia levels in patients undergoing cardiac surgery, the bispectral index and an electromyogram-based surrogate variable are proposed as the controlled variables. The study is carried on in the context of implementing a multivariable predictive control algorithm. The simulation results show that such a paradigm is feasible, although it does not guarantee perfect knowledge of the analgesia level-in other words, the variable is not validated against typical evaluations of the pain levels (e.g. clinical scores).

An enriched simulation environment for evaluation of closed-loop anesthesia

To simulate and evaluate the administration of anesthetic agents in the clinical setting, many pharmacology models have been proposed and validated, which play important roles for in silico testing of closed-loop control methods. However, to the authors' best knowledge, there is no anesthesia simulator incorporating closed-loop feedback control of anesthetic agent administration freely available and accessible to the public. Consequently, many necessary but time consuming procedures, such as selecting models from the available literatures and establishing new simulator algorithms, will be repeated by different researchers who intend to explore a novel control algorithm for closed-loop anesthesia. To address this issue, an enriched anesthesia simulator was devised in our laboratory and made freely available to the anesthesia community. This simulator was built by using MATLAB(®) (The MathWorks, Natick, MA). The GUI technology embedded in MATLAB was chosen as the tool to develop a human-machine interface. This simulator includes four types of anesthetic models, and all have been wildly used in closed-loop anesthesia studies. For each type of model, 24 virtual patients were created with significant diversity. In addition, the platform also provides a model identification module and a control method library. For the model identification module, the least square method and particle swarm optimization were presented. In the control method library, a proportional-integral-derivative control and a model predictive control were provided. Both the model identification module and the control method library are extensive and readily accessible for users to add user-defined functions. This simulator could be a benchmark-testing platform for closed-loop control of anesthesia, which is of great value and has significant development potential. For convenience, this simulator is termed as Wang's Simulator, which can be downloaded from http://www.AutomMed.org .

Robust closed-loop control of propofol administration using WAVCNS index as the controlled variable

This paper presents a robust closed-loop strategy for control of depth of hypnosis. The proposed method regulates the electroencephalogram (EEG)-derived WAVCNS index as a hypnosis measure by manipulating intravenous propofol administration. In contrast to many existing closed-loop methods, the control design presented in this paper produces stability and robustness against uncertainty by explicitly accounting for the pharmacokinetic (PK) and pharmacodynamic (PD) variability between different individuals, as well as unpredictable surgical stimuli that the closed-loop control is required to tolerate. This closed-loop control was evaluated using simulated surgical procedures in 44 patient models whose PK and PD were identified from real clinical data. The controller can deliver consistent and acceptable closed-loop induction and maintenance phase responses for patients with wide-ranging PK and PD differences.