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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.

This paper presents a new methodology to solve a Closed-Loop Supply Chain (CLSC) management problem through a decision-making system based on fuzzy logic built on machine learning. The system will provide decisions to operate a production plant integrated in a CLSC to meet the production goals with the presence of uncertainties. One of the main contributions of the proposal is the ability to reject the effects that the imbalances in the rest of the chain have on the inventories of raw materials and finished products. For this, an intelligent algorithm will be in charge of the supervision of the plant operation and task-reprogramming to ensure the achievement of the process goals. Fuzzy logic and machine learning techniques are combined to design the tool. The method was tested on an industrial hospital laundry with satisfactory results, thus highlighting the potential of this proposal for its incorporation into the Industry 4.0 framework.

Background and Objective: New proposals to improve the regulation of hypnosis in anaesthesia based on the development of advanced control structures emerge continuously. However, a fair study to analyse the real benefits of these structures compared to simpler clinically validated PID-based solutions has not been presented so far. The main objective of this work is to analyse the performance limitations associated with using a filtered PID controller, as compared to a high-order controller, represented through a Youla parameter. Methods: The comparison consists of a two-steps methodology. First, two robust optimal filtered PID controllers, considering the effect of the inter-patient variability, are synthesised. A set of 47 validated paediatric pharmacological models, identified from clinical data, is used to this end. This model set provides representative inter-patient variability Second, individualised filtered PID and Youla controllers are synthesised for each model in the set. For fairness of comparison, the same performance objective is optimised for all designs, and the same robustness constraints are considered. Controller synthesis is performed utilising convex optimisation and gradient-based methods relying on algebraic differentiation. The worst-case performance over the patient model set is used for the comparison. Results: Two robust filtered PID controllers for the entire model set, as well as individual-specific PID and Youla controllers, were optimised. All considered designs resulted in similar frequency response characteristics. The performance improvement associated with the Youla controllers was not significant compared to the individually tuned filtered PID controllers. The difference in performance between controllers synthesized for the model set and for individual models was significantly larger than the performance difference between the individual-specific PID and Youla controllers. The different controllers were evaluated in simulation. Although all of them showed clinically acceptable results, the robust solutions provided slower responses. Conclusion: Taking the same clinical and technical considerations into account for the optimisation of the different controllers, the design of individual-specific solutions resulted in only marginal differences in performance when comparing an optimal Youla parameter and its optimal filtered PID counterpart. The inter-patient variability is much more detrimental to performance than the limitations imposed by the simple structure of the filtered PID controller.

The high concern about climate change has boosted the promotion of renewable energy systems, being the wind power one of the key generation possibilities in this field. In this context, with the aim of ensuring the energy efficiency, the present work deals with the fault detection in the power electronic circuits of a wind generator system placed in a bioclimatic house. To do so, different outliers that emulate harmonic distortion appearance are tested. To implement a system capable of detecting this anomalous situations, six different one-class techniques are used, whose performance is thoroughly analyzed, offering interesting performance. info:eu-repo/semantics/publishedVersion

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.

[Resumen] En este trabajo se presenta una metodología para desarrollar un gemelo digital para un proceso industrial. El proceso es una lavandería industrial hospitalaria que puede ser caracterizada como una cadena de suministro de lazo cerrado. Una característica del proceso estudiado es la presencia de incertidumbre en la entrada, lo que dificulta la toma de decisiones en el proceso de producción. Para facilitar esta toma de decisiones se plantea el desarrollo de un gemelo digital basado en un sistema de lógica borrosa multinivel. La evaluación de la herramienta propuesta se ha hecho con datos reales obtenidos de una lavandería. Se ha comprobado que la propuesta ofrece resultados satisfactorios demostrando el potencial de esta herramienta en los procesos de producción. [Abstract] This paper presents a methodology to develop a digital twin for an industrial process. The process is a hospital industrial laundry, that can be characterized as a Closed-loop supply chain. A characteristic of the process studied is the presence of uncertainty in the input, which makes decision making in the production process difficult. To facilitate this decision making, the development of a Digital Twin based on a multilevel fuzzy logic system is considered. The evaluation of the proposed tool has been done with real data obtained from a laundry. It has been proven that the proposal offers satisfactory results, demonstrating the potential of this tool in the production processes.

Current advanced control strategies developed in Adaptive Optics field are based on optimal techniques in order to reject the effect of atmosphere turbulence on the phase of wavefront of the incoming light. The widely researched LQG controller relies basically on models that try to capture the temporal evolution of the atmospheric wavefront. Models based on both Cn2 spatial priors and temporal dynamics have been proposed for SCAO and MOAO systems. The attractive performance of LQG is based on having an accurate model of the wavefront distortion. However, abrupt changes in conditions that directly affect the atmosphere produce a significant decrease in the LQG performance. To deal with this problem, an Active Disturbance Rejection Control (ADRC) strategy is proposed. ADRC, is an unconventional design strategy capable of overcoming the internal dynamics of the system and the external disturbances. The key idea is to treat the unknown information mainly due to atmosphere turbulence as state variable that can be estimated by an Extended State Observer (ESO). Instead of depending on a model, the controller draws the information needed to control the system from the ESO. Consequently, the control law for the Adaptive Optics system can be designed without the mathematical expression of the disturbances. As a result, the combined effect of the non-considered internal dynamics and the external disturbances is estimated and actively rejected. This contribution is focused on adapting ADRC method to Adaptive Optics requirements.

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.