Identification and targeting policies for computer-controlled infusion pumps.

In this article a series of useful techniques is presented to improve the adaptation capabilities of computer-controlled infusion pumps: an identification algorithm for the time constant of the effect compartment, a smoothing technique for estimation based on noisy data, and an infusion policy to target any effect site concentration with no overshoot.

Artifact-tolerant controllers for automatic drug delivery in anesthesia.

This article presents a method for treating measurement artifacts in model-based control systems. A nonlinear modification to the usual observer structure is introduced to prevent the measurement artifacts from winding up the controller states. It is shown how stability of the closed loop system can be analyzed and an example of a successful application in a clinical study is provided.

Improving regulation of mean arterial blood pressure during anesthesia through estimates of surgery effects.

In this paper, a scheme for improvement of the regulation of mean arterial blood pressure (MAP) during anesthesia based on model predictive control (MPC) and estimates of the effects of disturbances (surgical events) is proposed. A linear model for the combined effects of surgical stimulations and volatile anesthetics on MAP is derived from experimental data. Based on it the potential improvement in blood pressure regulation is evaluated via a simulation study. The simulation study shows that when information about the effect of the surgical events on MAP is utilized by the controller maximum MAP deviations can be reduced by as much as 50% even when this information is inaccurate. At worst, (highly inaccurate information) no improvement is obtained.

Modeling and closed-loop control of hypnosis by means of bispectral index (BIS) with isoflurane.

A model-based closed-loop control system is presented to regulate hypnosis with the volatile anesthetic isoflurane. Hypnosis is assessed by means of the bispectral index (BIS), a processed parameter derived from the electroencephalogram. Isoflurane is administered through a closed-circuit respiratory system. The model for control was identified on a population of 20 healthy volunteers. It consists of three parts: a model for the respiratory system, a pharmacokinetic model and a pharmacodynamic model to predict BIS at the effect compartment. A cascaded internal model controller is employed. The master controller compares the actual BIS and the reference value set by the anesthesiologist and provides expired isoflurane concentration references to the slave controller. The slave controller maneuvers the fresh gas anesthetic concentration entering the respiratory system. The controller is designed to adapt to different respiratory conditions. Anti-windup measures protect against performance degradation in the event of saturation of the input signal. Fault detection schemes in the controller cope with BIS and expired concentration measurement artifacts. The results of clinical studies on humans are presented.

Model-based automatic feedback control versus human control of end-tidal isoflurane concentration using low-flow anaesthesia.

We studied the clinical use of an automatic feedback control system to adjust the end-tidal anaesthetic concentration with a low-flow method. The end-tidal controller uses two input signals (the end-tidal and inspiratory concentrations) to control the isoflurane concentration in the fresh gas flow, using a model-based algorithm. We studied 22 ASA I-III patients during elective surgery lasting more than 2 h. The anaesthetist was asked to make four step changes of the target end-tidal concentration (+0.3, +0.6, -0.3, -0.6 vol%), either manually (Group A) or by setting the target value for the feedback controller (Group B), and then the control was changed and the step changes were repeated, in a crossover design. Eighty step changes with each control method were compared in terms of response time, maximal overshoot and stability. The automatic control system was more accurate and stable than the human controller for step increases and step decreases, with less overshoot/undershoot and greater stability [e.g. maximal overshoot 14.7 (SD 3.7)% and 18 (8.1)% respectively for +0.6 vol% step changes, and 19.8 (3.7)% and 30.7 (13.2)% respectively for +0.3 vol% step changes]. However, the automatic control system showed a faster response time than the manual method only with large increasing steps (e.g. 149 (32) s and 205 (57) s respectively for +0.6 vol% step changes) and was not different from manual control for decreasing steps. Automatic control of the end-tidal isoflurane concentration can be better than human control in a clinical setting, and this task could be done automatically.