José Antonio Reboso

Adaptive computer control of anesthesia in humans

This paper presents an efficient computer control technique for regulation of anesthesia in humans. The anesthetic used is propofol and the objective is to control the degree of hypnosis of the patient. The paper describes the basic hardware/software setup of the system and the closed-loop methodologies. The bispectral index (BIS) is considered as the feedback signal. The control methods proposed here are based in the use of proportional integral controllers with dead-time compensation to avoid undesirable oscillations in the BIS signal during the process. The compensation is based on the Smith predictor. To guarantee the applicability of the method to different patients, an adaptive module to tune the compensator is developed. Some real and simulated results are presented in this work to attest the efficiency of the methods used.

Design and implementation of a closed-loop control system for infusion of propofol guided by bispectral index (BIS)

This study describes the design of a hypnosis closed‐loop control system with propofol. The controller used a proportional‐integral (PI) algorithm with the bispectral index (BIS) as the feedback signal. Our hypothesis was that a PI closed‐loop control could be applied in clinical practice safely keeping the BIS within a pre‐determined target range.

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.

Model-based controller for anesthesia automation

This paper presents an approach to anesthesia control using a model-based controller. General anesthesia with propofol is considered. The proposal tries to take advantage of the benefits of model-based controllers to improve the performance of control in anesthesia. The controller proposed is based on the application of two control actions. First, a nominal term is applied obtained from the inverse dynamics model. This action is corrected by adding a second term that compensates modeling errors, disturbances, etc. To compute the correction, a linearization of the model is considered around the nominal state and optimization is performed to compute the control action. Several results obtained in simulation are presented to test the efficiency of the method.

A Novel Fuzzy Algorithm to Introduce New Variables in the Drug Supply Decision-Making Process in Medicine

One of the main challenges in medicine is to guarantee an appropriate drug supply according to the real needs of patients. Closed-loop strategies have been widely used to develop automatic solutions based on feedback variables. However, when the variable of interest cannot be directly measured or there is a lack of knowledge behind the process, it turns into a difficult issue to solve. In this research, a novel algorithm to approach this problem is presented. The main objective of this study is to provide a new general algorithm capable of determining the influence of a certain clinical variable in the decision making process for drug supply and then defining an automatic system able to guide the process considering this information. Thus, this new technique will provide a way to validate a given physiological signal as a feedback variable for drug titration. In addition, the result of the algorithm in terms of fuzzy rules and membership functions will define a fuzzy-based decision system for the drug delivery process. The method proposed is based on a Fuzzy Inference System whose structure is obtained through a decision tree algorithm. A four-step methodology is then developed: data collection, preprocessing, Fuzzy Inference System generation, and the validation of results. To test this methodology, the analgesia control scenario was analysed. Specifically, the viability of the Analgesia Nociception Index (ANI) as a guiding variable for the analgesic process during surgical interventions was studied. Real data was obtained from fifteen patients undergoing cholecystectomy surgery.

Remifentanil Dose Prediction for Patients During General Anesthesia

In the anesthesia field there are some challenges, such as achieving new methods to control, and, of course, for reducing the pain suffered for the patients during surgeries. The first steps in this field were focused on obtaining representative measurements for pain measurement. Nowadays, one of the most promiser index is the ANI (Antinociception Index). This research works deals the model for the remifentanil dose prediction for patients undergoing general anesthesia. To do that, a hybrid model based on intelligent techniques is implemented. The model was trained using Support Vector Regression (SVR) and Artificial Neural Networks (ANN) algorithms. Results were validated with a real dataset of patients. It was possible to check the really successful model performance.

Improving the anesthetic process by a fuzzy rule based medical decision system

OBJECTIVE The main objective of this research is the design and implementation of a new fuzzy logic tool for automatic drug delivery in patients undergoing general anesthesia. The aim is to adjust the drug dose to the real patient needs using heuristic knowledge provided by clinicians. A two-level computer decision system is proposed. The idea is to release the clinician from routine tasks so that he can focus on other variables of the patient. METHODS The controller uses the Bispectral Index (BIS) to assess the hypnotic state of the patient. Fuzzy controller was included in a closed-loop system to reach the BIS target and reject disturbances. BIS was measured using a BIS VISTA monitor, a device capable of calculating the hypnosis level of the patient through EEG information. An infusion pump with propofol 1% is used to supply the drug to the patient. The inputs to the fuzzy inference system are BIS error and BIS rate. The output is infusion rate increment. The mapping of the input information and the appropriate output is given by a rule-base based on knowledge of clinicians. RESULTS To evaluate the performance of the fuzzy closed-loop system proposed, an observational study was carried out. Eighty one patients scheduled for ambulatory surgery were randomly distributed in 2 groups: one group using a fuzzy logic based closed-loop system (FCL) to automate the administration of propofol (42 cases); the second group using manual delivering of the drug (39 cases). In both groups, the BIS target was 50. CONCLUSIONS The FCL, designed with intuitive logic rules based on the clinician experience, performed satisfactorily and outperformed the manual administration in patients in terms of accuracy through the maintenance stage.

Closed-Loop Control of Anaesthetic Effect

Archivo disponible en la web de la revista, Open Access, en la siguiente URL: https://www.intechopen.com/books/pharmacology/closed-loop-control-of-anesthetic-effect Se puede referenciar de la siguiente manera: Santiago Torres, Juan A. Mendez, Hector Reboso, Jose A. Reboso and Ana Leon (2012). Closed-Loop Control of Anaesthetic Effect, Pharmacology, Dr. Luca Gallelli (Ed.), InTech, DOI: 10.5772/37609. Available from: https://www.intechopen.com/books/pharmacology/closed-loop-control-of-anestheti

Predictive algorithm for intravenous anesthesia control

This work deals with anesthesia control in humans. The control problem is to regulate the hypnosis state of the patient around a target specified by the anesthetist. The drug used here is propofol and the controller will work in general anesthesia conditions. As a preliminary study, real-time results with PI control are presented to demonstrate the limitations of this strategy. As an alternative, this paper introduces a model based predictive control to regulate the hypnosis depth. The basis of the algorithm is to combine two terms to compute the control law. One is obtained from the inverse dynamics of the patient and the other is obtained from a predictive controller that corrects the deviations of the controlled variable. The goal is to show the applicability of the proposed strategy and to demonstrate the increase in performance when compared to signal based controllers. The paper presents For this, real and simulated results are presented in the paper.

Dead-time compensation in intravenous anesthesia control

This paper presents preliminary results of anesthesia control experiments in humans. The drug used is propofol and the administration is intravenous. The objective is to regulate the hypnosis depth in the patient. To achieve this, the Bispectral Index is taken as the feedback signal. In this work, results of the pharmacokinetics and pharmacodynamics modelling of the patient are presented. Physiological models have been considered and simulation tools have been used for validation. Results with Proportional Integral controllers are presented. Then the algorithm is modified with a dead-time compensator to improve the transitory response of the Bispectral Index. The results are compared to check the benefits of the compensator. A further step in the algorithm is the inclusion of an adaptive scheme so that the compensator designed can be adapted to different patients.