Ramona Hodrea

Variable Time-Delay Estimation for Anesthesia Control During Intensive Care

The presence of artifacts plays a crucial role in automatic sedation systems and may introduce variable time delays (TDs) in the closed-loop-control structures. This paper presents a successful procedure to estimate the varying TD of the bispectral index (BIS) monitor used in closed-loop control during intensive care. The TD estimation (TDE) is based on the cross-correlation analysis technique and the method is validated with real measured signals of propofol and BIS. Extended prediction self-adaptive control is used in combination with a Smith predictor to reduce the computational burden imposed by the variable TD. The conclusion is that an online TDE of the BIS monitor improves the performance of the closed-loop system for reference tracking, disturbance rejection, and overall stability.

Estimating the time-delay for predictive control in general anesthesia

Monitoring and controlling the depth of general anesthesia is very important since over-dosing and under-dosing can be dangerous for the well-being of the patient. One of the devices used to monitor the depth of anesthesia is the BIS (Bispectral Index) monitor. The relation between the BIS signal, measured by the BIS monitor, and the effect of the administered drugs is given by a function called Hill curve. The aim of this contribution is to implement a procedure for i) the estimation of the time-delay introduced by the computational effort employed by the BIS monitor and ii) the estimation of the parameters of a linear Hill curve. The estimation algorithm is implemented based on the cross-correlation analysis (CCA) between a real BIS signal and a simulated BIS signal. Initially, the algorithm was validated using only simulated signals, followed by a clinical study. The estimated time-delay will be used further in the prediction model of the Extended Prediction Self-Adaptive Control (EPSAC) algorithm.

Modeling and internal model control strategy of pH neutralization process

This paper presents a possible application of controlling industrial pH processes using internal model control strategy. The developed strategy consists in including the neutralization model within the control structure. In this work a pH neutralization process for acetic acid and sodium hydroxide model is developed in Matlab/SIMULINK simulation environment. The proposed controller is implemented using internal model control strategy design. Process simulations and control loop validation plots are presented.

Wiener model identification for muscle relaxation

This paper presents a Wiener model developed for muscle relaxation. The model consists of a linear transfer function and a nonlinear block based on a sigmoid network expansion. Data collected during surgery at Regional Institute of Gastroenterology and Hepatology “Prof. Dr. Octavian Fodor” are used to identify the parameters of this model. The input is atracurium, an intermediate-duration non-depolarizing neuromuscular-blocking agent used in anaesthesia to facilitate endotracheal intubation and to provide skeletal muscle relaxation during surgery or mechanical ventilation. The output is the amplitude of the first twitch, a variable measured with the TOF-Watch SX device. The model identified proved to describe accurately the metabolism of atracurium.

Impact of disturbance filter in nonlinear EPSAC predictive control of blood glucose level in type I diabetic patients

The nonlinear EPSAC (NEPSAC) predictive control algorithm has been implemented to regulate blood glucose level in type I diabetic patients, by controlling the insulin infusion rate with a micropump. The advantage of the NEPSAC strategy is that the nonlinear process is never linearized, thus it uses directly the nonlinear patient model. The role of the disturbance filter in model-based predictive control is underlined and its possibility to improve control performance is exploited. A default disturbance filter, consisting of an integrator to ensure zero steady-state error, is compared to an intelligent filter design with and without output constraints. Controller performance was assessed in terms of its ability to reject a meal disturbance of 30 g glucose.

EPSAC versus PID control of neuromuscular blockade

The paper deals with the implementation of two controllers for the administration of neuromuscular blockade agents in general anaesthesia: Proportional-Integral-Derivative (PID) and Extended Prediction Self-Adaptive Control (EPSAC). Predictive control requires prior knowledge of system dynamics, therefore a mathematical model is required. A Wiener model has been initially identified, using real data recorded during surgical interventions. The controllers were applied to 8 simulated patients and performance was analyzed and discussed for reference tracking and inter-patient variability.

Modeling of drug delivery in general anesthesia

Manual or open-loop administration does not take into account patients individual dose or dose-response relationship; hence they represent sub-optimal solutions for optimizing individual drug titration. This may lead to under-or over- sedation, increasing the time on mechanical ventilation, the length of intensive care unit (ICU) stay and mortality. Model based predictive control can mitigate with this problem, improving the efficiency of drug delivery and patient safety. A multiple-input single-output (MISO) patient model is identified and validated in this paper. The inputs are two drugs commonly used for general anesthesia, propofol and remifentanil, and the output is the Bispectral Index (BIS). Wavelet time-frequency analysis was used to filter the measured signals. The parameters of the interaction model which relates the effect-site concentrations of these drugs to BIS are identified based on least-squares algorithm, using data from real-life clinical tests.

EPSAC Predictive Control Applied to Muscle Relaxant Administration

Abstract An Extended Prediction Self-Adaptive Control (EPSAC) strategy is applied to the rocuronium administration, a neuromuscular blocking drug used in anaesthesia to facilitate endotracheal intubation and to provide skeletal muscle relaxation during surgery. A safe anaesthesia should provide comfort to the patient and best possible working conditions for the surgeon. The muscle relaxant plays an important role and an adequate muscle relaxation, which allows efficient and safe surgery, can be provided using automated control. Moreover, closed-loop control may reduce anesthetist workload and achieve better regulation of muscle relaxation than manual administration, calculating the dose required for each patient and avoiding in this way overdosing. Predictive control requires prior knowledge of system dynamics, therefore a mathematical model is required. A three-compartment model for rocuronium is presented. Several parameter sets are used to simulate different patients and control performance is discussed.

Predictive Control Strategy with Online Time Delay Estimation Applied in General Anaesthesia

Abstract General anaesthesia refers to the state of total unconsciousness resulted from the administration of several drugs. The depth of anaesthesia can be monitored with a device called BIS (Bispectral index) monitor. A manifold of artifacts corrupt the signals measured with this monitor, e.g. coughing, movement of feet and arms, face washing, etc., which challenge the correct estimation of the bispectral index. As such, estimation techniques evaluate the signal to noise ratio and if insufficient information is available, data from the past measurements is used to evaluate the current state of the patient. In this manner, instrumental time delay is introduced in the closed loop regulation of general anaesthesia. This paper evaluates whether or not an incorrect estimation of the time delay has an influence on the stability and robustness of the closed loop control. The performance of the EPSAC (Extended Prediction Self-Adaptive Control) controller was tested using different scenarios. Under- and over-estimations of the real time delay were considered in the prediction. The online time delay estimation was added to the control algorithm and the performance was evaluated.

Model reduction for adaptation in surgery anaesthesia

Abstract This paper emphasizes the importance of model reduction when using adaptive control techniques in surgery anaesthesia. Most of the modeling tools lead to sophisticated models, which are not suitable for adaptation due to their complexity. Such complex patient models, describing the relationship between the drug administration during anaesthesia and its effect, exist in the literature, but before applying adaptive strategies for drug dosing control, reduction is required. Several reduced models were obtained from the compartmental 4 th order model initially developed by Schnider, through analysis of various time constants. The most simplified patient model was obtained by identifying two identical poles. To test the accuracy of the prospective reduced models, the EPSAC predictive control algorithm was applied on the original 4 th order model used as patient simulator, while using the simplified models for prediction. An analysis of the closed-loop response is given, showing that the reduced model is a good approximation of the original system and it can be effectively used in a closed-loop adaptive control strategy for Propofol delivery during anaesthesia.