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This work focuses on the application of machine learning techniques to assist the clinicians in the administration of analgesic drug during general anaesthesia. Specifically, the main objective is to propose the basis of an intelligent system capable of making decisions to guide the opioid dose changes based on a new nociception monitor, the analgesia nociception index (ANI). Clinical data were obtained from 15 patients undergoing cholecystectomy surgery. By means of an off-line study, machine learning techniques were applied to analyse the possible relationship between the analgesic dose changes performed by the physician due to the hemodynamic activity of the patients and the evolution of the ANI. After training different classifiers and testing the results under cross validation, a preliminary relationship between the evolution of ANI and the dosage of remifentanil was found. These results evidence the potential of the ANI as a promising index to guide the infusion of analgesia.

Automatic control of physiological variables is one of the most active areas in biomedical engineering. This paper is centered in the prediction of the analgesic variables evolution in patients undergoing surgery. The proposal is based on the use of hybrid intelligent modelling methods. The study considers the Analgesia Nociception Index (ANI) to assess the pain in the patient and remifentanil as intravenous analgesic. The model proposed is able to make a one-step-ahead prediction of the remifentanil dose corresponding to the current state of the patient. The input information is the previous remifentanil dose, the ANI variable and the electromyogram signal. Modelling techniques used are Artificial Neural Networks and Support Vector machines for Regression combined with clustering methods. Both training and validation were done with a real dataset from different patients. Results obtained show the potential of this methodology to calculate the drug dose corresponding to a given analgesic state of the patient.

The availability of accurate models for predicting the drug effect in patients undergoing general anesthesia is an important factor in producing a personalized drug infusion. These models should consider different clinical factors to provide realistic predictions. This paper proposes a new methodology for modeling the depth of hypnosis (DOH) during anesthesia. The model, which is based on a pharmacokinetic–pharmacodynamic structure, explicitly takes into account the interaction between the hypnotic and opioid drugs delivered during surgery. Patients undergoing general surgery with intravenous propofol–remifentanil anesthesia were considered. The bispectral index (BIS) was used for monitoring the DOH. In contrast with previous research, the uniqueness of this study lies in the proposal of an adaptive model to deal simultaneously with the variabilities in the clinical response of the patients, the drug interactions, and the variable time delay introduced by the BIS monitor. The proposed method was validated using data from 17 patients undergoing general anesthesia. Successful results were obtained for predicting the evolution of BIS during the induction and maintenance phases of propofol–remifentanil anesthesia. Specifically, the convenience of an adaptive model that included all the factors likely to affect the anesthetic process was demonstrated. The proposed methodology can be used for the development of new models to be employed in model predictive control strategies for closed-loop anesthesia.

The use of Hybrid Artificial Intelligent techniques in medicine has increased in recent years. Specifically, one of the main challenges in anesthesia is achieving new controllers capable of automating the drug titration during surgeries. This work deals with the development of a Takagi-Sugeno fuzzy controller to automate the drug infusion for the control of hypnosis in patients undergoing anesthesia. To do that, a combination of Neural Networks and optimization techniques were applied to tune the internal parameters of the fuzzy controller. For the training process, data from 20 patients undergoing surgery were used. Finally, the controller proposed was tested over 16 virtual surgeries. It was concluded that the fuzzy controller was able to meet both clinical and control objectives.

Measuring the level of analgesia to adapt the opioids infusion during anesthesia to the real needs of the patient is still a challenge. This is a consequence of the absence of a specific measure capable of quantifying the nociception level of the patients. Unlike existing proposals, this paper aims to evaluate the suitability of the Analgesia Nociception Index (ANI) as a guidance variable to replicate the decisions made by the experts when a modification of the opioid infusion rate is required. To this end, different machine learning classifiers were trained with several sets of clinical features. Data for training were captured from 17 patients undergoing cholecystectomy surgery. Satisfactory results were obtained when including information about minimum values of ANI for predicting a change of dose. Specifically, a higher efficiency of the Support Vector Machine (SVM) classifier was observed compared with the situation in which the ANI index was not included: accuracy: 86.21% (83.62%–87.93%), precision: 86.11% (83.78%–88.57%), recall: 91.18% (88.24%–91.18%), specificity: 79.17% (75%–83.33%), AUC: 0.89 (0.87–0.90) and kappa index: 0.71 (0.66–0.75). The results of this research evidenced that including information about the minimum values of ANI together with the hemodynamic information outperformed the decisions made regarding only non-specific traditional signs such as heart rate and blood pressure. In addition, the analysis of the results showed that including the ANI monitor in the decision making process may anticipate a dose change to prevent hemodynamic events. Finally, the SVM was able to perform accurate predictions when making different decisions commonly observed in the clinical practice.

BACKGROUND Delay in the propofol pharmacodynamics effect is commonly observed in total intravenous anesthesia (TIVA). To face the delay in the hypnosis control, we have proposed a proportional-integral (PI) controller with a Smith predictor (PI+Smith). We have evaluated the feasibility of this closed-loop control for propofol administration and compared the performance with manual administration guided by the Bispectral Index (BIS). METHODS Fifty-seven adult patients under TIVA with propofol and remifentanil were randomly assigned to a PI+Smith or a manual control (MC) group. The BIS target was set to 50. The performance was compared through the global score (GS), median performance error (MDPE), median absolute performance error (MDAPE), offset and Wobble. RESULTS A total of 29 patients in the MC and 25 in the PI+Smith groups completed this study. Performance was significantly better in the PI+Smith group: global score was 25 (19 to 37) for PI+Smith versus 44 (32 to 57) for MC (P

The problem of automating the infusion of anesthesia using fuzzy predictive control techniques is afforded. The control objective is to keep the hypnosis level of the patient in a proper and safe value. To provide accurate predictions, an adaptive model based on fuzzy logic and genetic algorithms is included. Thus, the drug infusion is adapted to the real needs of the patient and, consequently, the performance compared to other approaches is improved. The controller was evaluated both in simulation and in the operating room with patients undergoing surgery. Results obtained attest for the efficiency of the proposed method.

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.

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.

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.

Background 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. Methods The adjustment of the PI parameters was based on simulation. The procedure had three steps: obtaining a patient model using data from 12 patients, designing and adjusting the controller in simulation, and fine tuning the PI parameters in a pilot study (10 patients). The resulting controller was tested in 24 American Society of Anesthesiology (ASA) I–II patients. The controller directly decides the infusion rate of propofol, and no model is necessary in its online operation. The BIS target was set to 50. Remifentanil was used for analgesia. Results We evaluated the efficiency and safety of the automatic feedback system. It worked properly in all the patients. The median performance error was −1.62, and the median absolute performance error was 11.03. Average propofol-normalized consumption was 5.3 ± 1.8 mg/kg/h. Mean percentage of BIS in the range 40–60 was 83%. Mean time to open eyes was 8 ± 4 min. Time to extubation was 9 ± 5 min. Hemodynamic adverse event or intraoperative awareness were not recorded. Conclusions The closed-loop system was able to maintain the BIS within an acceptable range of levels. The control of a propofol infusion guided by the BIS is feasible without hemodynamic instability in ASA I/II patients.

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.

Using Schnider's pharmacokinetic model, propofol pharmacodynamics were modelled during total intravenous anaesthesia. The method involved adjusting a pharmacokinetic/pharmacodynamic model according to data obtained from 42 patients having operative procedures with remifentanil analgesia. Parameters Ce50 and γ were estimated for induction and maintenance by analysing patients' bispectral index. The pharmacodynamic models were different for induction and maintenance. The mean (95% CI) Ce50 for induction and maintenance was Ce50 = 3.35 (2.79-3.91) mg.l(-1) and 2.23 (1.95-2.51) mg.l(-1) , respectively, with a higher concentration required to achieve the same effect during induction, even during remifentanil co-administration. During induction and maintenance, γ was 1.24 (1.44-2.00) and 1.58 (1.32-1.84), respectively. As γ is related to the concentration-effect slope, patient response is accentuated during maintenance compared with induction. The influence of sex and age on the model was analysed. Sex had no significant influence on the model, although a linear relation was found between age and Ce50 .

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

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.

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.

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.

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.

[ES] Este artículo se centra en el modelado y control de la hipnosis durante la anestesia de pacientes sometidos a intervenciones quirúrgicas. Por un lado en este trabajo se aborda el problema del modelado del proceso presentando resultados validados con pacientes reales. Asimismo, se propone un controlador avanzado para regular el estado hipnótico. El algoritmo empleado se basa en combinar una acción nominal obtenida a partir de la dinámica inversa juntamente con una acción correctora que se obtiene a partir de un controlador predictivo. El trabajo persigue desarrollar una técnica que permita la regulación del estado del paciente y que tenga características de adaptabilidad a los diferentes individuos. Se muestran resultados preliminares de la estrategia propuesta para demostrar la eficiencia del sistema., [EN] This paper is focused on modeling and control the hypnosis of patients undergoing to anaesthesia during surgery. On the one hand, this paper addresses the problem of modeling the process and shows results validated with real patients. It also proposes an advanced controller to regulate the patient hypnosis. The proposed algorithm is based on combining a nominal input, obtained from the inverse dynamics of the patient model, together with a corrective action derived from a predictive controller. The aim of this work is to develop a technique for regulating the patient's degree of hypnosis, being the control technique adaptable to different individuals. Preliminary results of the proposed strategy demonstrate the efficiency of the system with the proposed algorithm., Este trabajo ha sido financiado con fondos del Ministerio de Ciencia e Innovación a través del proyecto del plan nacional de investigación DPI2010-18278.