Manual de litografía

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A novel wavelet-based filtering strategy to remove powerline interference from electrocardiograms with atrial fibrillation

This is an author-created, un-copyedited versíon of an article published in Physiological Measurement. IOP Publishing Ltd is not responsíble for any errors or omissíons in this versíon of the manuscript or any versíon derived from it. The Versíon of Record is available online at http://doi.org/10.1088/1361-6579/aae8b1[EN] Objective: The electrocardiogram (ECG) is currently the most widely used recording to diagnose cardiac disorders, including the most common supraventricular arrhythmia, such as atrial fibrillation (AF). However, different types of electrical disturbances, in which power-line interference (PLI) is a major problem, can mask and distort the original ECG morphology. This is a significant issue in the context of AF, because accurate characterization of fibrillatory waves (f-waves) is unavoidably required to improve current knowledge about its mechanisms. This work introduces a new algorithm able to reduce high levels of PLI and preserve, simultaneously, the original ECG morphology. Approach: The method is based on stationary wavelet transform shrinking and makes use of a new thresholding function designed to work successfully in a wide variety of scenarios. In fact, it has been validated in a general context with 48 ECG recordings obtained from pathological and non-pathological conditions, as well as in the particular context of AF, where 380 synthesized and 20 long-term real ECG recordings were analyzed. Main results: In both situations, the algorithm has reported a notably better performance than common methods designed for the same purpose. Moreover, its effectiveness has proven to be optimal for dealing with ECG recordings affected by AF, sincef-waves remained almost intact after removing very high levels of noise. Significance: The proposed algorithm may facilitate a reliable characterization of thef-waves, preventing them from not being masked by the PLI nor distorted by an unsuitable filtering applied to ECG recordings with AF.Research supported by grants DPI2017-83952-C3 MINECO/AEI/FEDER, UE and SBPLY/17/180501/000411 from Junta de Comunidades de Castilla-La Mancha.García, M.; Martínez, M.; Ródenas, J.; Rieta, JJ.; Alcaraz, R. (2018). A novel wavelet-based filtering strategy to remove powerline interference from electrocardiograms with atrial fibrillation. Physiological Measurement. 39(11):1-15. https://doi.org/10.1088/1361-6579/aae8b1S115391

Design and Implementation of an AI-Enabled Sensor for the Prediction of the Behaviour of Software Applications in Industrial Scenarios

In the era of Industry 4.0 and 5.0, a transformative wave of softwarisation has surged. This shift towards software-centric frameworks has been a cornerstone and has highlighted the need to comprehend software applications. This research introduces a novel agent-based architecture designed to sense and predict software application metrics in industrial scenarios using AI techniques. It comprises interconnected agents that aim to enhance operational insights and decision-making processes. The forecaster component uses a random forest regressor to predict known and aggregated metrics. Further analysis demonstrates overall robust predictive capabilities. Visual representations and an error analysis underscore the forecasting accuracy and limitations. This work establishes a foundational understanding and predictive architecture for software behaviours, charting a course for future advancements in decision-making components within evolving industrial landscapes.This work was funded in part by the European Commission Horizon 2020 5G-PPP Program under Grant Agreement Number H2020-ICT-2020-2/101017226: “6G BRAINS: Bringing Reinforcement learning Into Radio Light Network for Massive Connections” and the EU Horizon INCODE project Programming Platform for Intelligent Collaborative Deployments over Heterogeneous Edge IoT Environments (HORIZON-CL4-2022-DATA-01-03/101093069)

ServiceNet:resource-efficient architecture for topology discovery in large-scale multi-tenant clouds

Modern computing infrastructures are evolving due to virtualisation, especially with the advent of 5G and future technologies. While this transition offers numerous benefits, it also presents challenges. Consequently, understanding these complex systems, including networks, services, and their interconnections, is crucial. This paper introduces ServiceNet, a groundbreaking architecture that accurately performs the important task of providing understanding of a multi-tenant architecture by discovering the complete topology, crucial in the realm of high-performance distributed computing. Experimental results have been carried out in different scenarios in order to validate our approach, demonstrating the effectiveness of our approach in comprehensive multi-tenant topology discovery. The experiments, involving up to forty tenant, highlight the adaptability of ServiceNet as a valuable tool for real-time monitoring in topology discovery purposes, even in challenging scenarios

The Effect of Adverse Surgical Margins on the Risk of Biochemical Recurrence after Robotic-Assisted Radical Prostatectomy

Positive surgical margins (PSM) after radical prostatectomy are associated with a greater risk of biochemical recurrence (BCR). However, not all PSM harbour the same prognosis for recurrence. We aim to determine the impact of different PSM characteristics and their coexistence on the risk of BCR. This retrospective study included 333 patients that underwent robotic-assisted radical prostatectomy for prostate cancer between 2015-2020 at a single institution. The effect of PSM and their adverse characteristics on the risk of BCR was assessed using Cox proportional hazard models. Kaplan-Meier was used to represent BCR-free survival stratified by margin status. With a median follow-up of 34.5 months, patients with PSM had a higher incidence of BCR, higher risk of relapse and lower BCR-free survival than negative margins (p < 0.001). We established as adverse characteristics: PSM length ≥ 3 mm, multifocality and Gleason at margin > 3. PSM ≥ 3 mm or multifocal PSM were associated with an increased risk for BCR compared to favourable margins (HR 3.50; 95% CI 2.05-5.95, p < 0.001 and HR 2.18; 95% CI 1.09-4.37, p = 0.028, respectively). The coexistence of these two adverse features in the PSM also conferred a higher risk for biochemical relapse and lower BCR-free survival. Adverse Gleason in the margin did not confer a higher risk for BCR than non-adverse margins in our models. We concluded that PSM are an independent predictor for BCR and that the presence of adverse characteristics, such as length and focality, and their coexistence in the PSM are associated with a greater risk of recurrence. Nevertheless, subclassifying PSM with adverse features did not enhance the model's predictive performance in our cohort

Formación a entrenadores de fútbol base y grado de satisfacción de los deportistas

The coach is one of the main figures in sports initiation continuity of the sport participation of the players. In this study an evaluation of the verbalizations of the trainer in a competition was proposed, along with a behavioral program. The purpose was to observe the behavioral changes in the trainer and examine if these would influence the satisfaction of the footballers. The verbalizations of three trainers during sixteen competition matches where taped, an adaptation of the CBAS was applied along with a self register of the trainers behaviors and a self register of the weekly targets of 3 trainers. The players of the 3 groups filled in a form that evaluated their levels of satisfaction regarding the behavior of their trainers.Results showed a decrease in the coaches’ negative verbal behaviors, and an increase in the athletes satisfaction after the behavior training program application, no matter the level of the competition. These results highlight the need to develop training programs for coaches in order to promote the involvement of players. El entrenador es una de las figuras clave en la iniciación deportiva influyendo en la continuidad de la participación deportiva. En este estudio se realizó una evaluación de las verbalizaciones del entrenador en competición y un programa de asesoramiento conductual. El objetivo del trabajo fue observar los cambios en la conducta del entrenador y examinar si esto repercutía en la satisfacción de los futbolistas. Para ello, se realizó la grabación de las verbalizaciones de 3 entrenadores durante 16 partidos de competición, se aplicó una adaptación del  instrumento CBAS y un autorregistro de conductas del entrenador. Los futbolistas rellenaron un cuestionario que evaluó su grado de satisfacción con respecto a la conducta de sus entrenadores. Los resultados mostraron una disminución de las conductas verbales con enfoque negativo por parte de los entrenadores, y un aumento en el grado de satisfacción de los deportistas tras la aplicación del programa de entrenamiento conductual con independencia de la categoría de competición. Estos resultados destacan la necesidad de desarrollar programas de asesoramiento en jóvenes entrenadores para favorecer la adherencia de los deportistas.

Searching for unknown counterparts in X-ray binary systems using Virtual Observatory tools

In the framework of an ongoing programme, we have developed strategies to discover and characterize optical/infrared unknown counterparts to X-ray binary systems using the standard tools of the Virtual Observatory. First, we have selected some potential candidates from different X-ray catalogues. Then we have used the Virtual Observatory tools to search for optical and infrared point data sources that were coincident with the position of the X-ray source. In this work we present some examples of our ongoing programme showing the potential of the Virtual Observatory as a discovery tool.Part of this work was supported by the Spanish Ministry of Education and Science project number AYA2010-15431, by the Vicerectorat d'Investigació, Desenvolupament i Innovació de la Universitat d'Alacant project number GRE12-35, and by the Generalitat Valenciana project number GV2014/088. JJRR acknowledges the support by the Matsumae International Foundation Research Fellowship program 2014, No14G04

Novel Entropy-Based Metrics for Long-Term Atrial Fibrillation Recurrence Prediction Following Surgical Ablation: Insights from Preoperative Electrocardiographic Analysis

[EN] Atrial fibrillation (AF) is a prevalent cardiac arrhythmia often treated concomitantly with other cardiac interventions through the Cox-Maze procedure. This highly invasive intervention is still linked to a long-term recurrence rate of approximately 35% in permanent AF patients. The aim of this study is to preoperatively predict long-term AF recurrence post-surgery through the analysis of atrial activity (AA) organization from non-invasive electrocardiographic (ECG) recordings. A dataset comprising ECGs from 53 patients with permanent AF who had undergone Cox-Maze concomitant surgery was analyzed. The AA was extracted from the lead V1 of these recordings and then characterized using novel predictors, such as the mean and standard deviation of the relative wavelet energy (RWEm and RWEs) across different scales, and an entropy-based metric that computes the stationary wavelet entropy variability (SWEnV). The individual predictors exhibited limited predictive capabilities to anticipate the outcome of the procedure, with the SWEnV yielding a classification accuracy (Acc) of 68.07%. However, the assessment of the RWEs for the seventh scale (RWEs7), which encompassed frequencies associated with the AA, stood out as the most promising individual predictor, with sensitivity (Se) and specificity (Sp) values of 80.83% and 67.09%, respectively, and an Acc of almost 75%. Diverse multivariate decision tree-based models were constructed for prediction, giving priority to simplicity in the interpretation of the forecasting methodology. In fact, the combination of the SWEnV and RWEs7 consistently outperformed the individual predictors and excelled in predicting post-surgery outcomes one year after the Cox-Maze procedure, with Se, Sp, and Acc values of approximately 80%, thus surpassing the results of previous studies based on anatomical predictors associated with atrial function or clinical data. These findings emphasize the crucial role of preoperative patient-specific ECG signal analysis in tailoring post-surgical care, enhancing clinical decision making, and improving long-term clinical outcomes.This research has received financial support from public grants PID2021-123804OB-I00, PID2021- 00X128525-IV0, and TED2021-130935B-I00 of the Spanish Government, 10.13039/501100011033, in conjunction with the European Regional Development Fund (EU), SBPLY/21/180501/000186, from Junta de Comunidades de Castilla-La Mancha, and AICO/2021/286 from Generalitat Valenciana. Pilar Escribano holds the 2020-PREDUCLM-15540 scholarship co-financed by the European Social Fund (ESF) operating program 2014 2020 of Castilla-La Mancha.Escribano, P.; Ródenas, J.; García, M.; Hornero, F.; Gracia-Baena, JM.; Alcaraz, R.; Rieta, JJ. (2024). Novel Entropy-Based Metrics for Long-Term Atrial Fibrillation Recurrence Prediction Following Surgical Ablation: Insights from Preoperative Electrocardiographic Analysis. Entropy. 26(1). https://doi.org/10.3390/e2601002826