Tratamiento digital de señales. Problemas y ejercicios resueltos

El documento es un libro de problemas y ejercicios de Tratamiento Digital de Señales. Este libro publicado por Prentice-Hall en 2003, se ofrece actualmente como recurso de acceso abierto tras su descatalogación. En él se ofrecen ejemplos de problemas y ejercicios resueltos de Tratamiento Digital de Señales, a los que previamente se introduce la base teórica suficiente como para seguir el desarrollo del texto. El contenido es el siguiente: Señales y sistemas en tiempo discreto; Análisis frecuencial de señales y sistemas; Transformada z; Realización de sistemas en tiempo discreto; Efectos de longitud de palabra finita; Diseño de filtros digitales; Sistemas adaptativos.That document is a book of problems and exercices of Digital Signal Processing. This book was published in 2003 by Prentice-Hall, and is now offered as an Open Acces resource after gotten out of catalog. It shows the resolution of problems and exercices of Digital Signal Processing, with a previous theoric introduction, enough to follow the text. The contents are: Discrete signals and systems; Frequencial analysis of signals and systems; Z Transform; Discrete-time systems implementation; Finite word-lenth effects; Digital filters design; Adaptative systems

Exploring the Heterogeneity and Trajectories of Positive Functioning Variables, Emotional Distress, and Post-traumatic Growth During Strict Confinement Due to COVID-19

COVID-19 pandemic-related confinement may be a fruitful opportunity to use individual resources to deal with it or experience psychological functioning changes. This study aimed to analyze the evolution of different psychological variables during the first coronavirus wave to identify the different psychological response clusters, as well as to keep a follow-up on the changes among these clusters. The sample included 459 Spanish residents (77.8% female, Mage = 35.21 years, SDage = 13.00). Participants completed several online self-reported questionnaires to assess positive functioning variables (MLQ, Steger et al. in J Loss Trauma 13(6):511–527, 2006. 10.1080/15325020802173660; GQ-6, McCullough et al. in J Person Soc Psychol 82:112–127, 2002. 10.1037/0022-3514.82.1.112; CD-RISC, Campbell-Sills and Stein in J Traum Stress 20(6):1019–1028, 2007. 10.1002/jts.20271; CLS-H, Chiesi et al. in BMC Psychol 8(1):1–9, 2020. 10.1186/s40359-020-0386-9; SWLS; Diener et al. in J Person Assess, 49(1), 71–75, 1985), emotional distress (PHQ-2, Kroenke et al. in Med Care 41(11):1284–1292, 2003. 10.1097/01.MLR.0000093487.78664.3C; GAD-2, Kroenke et al. in Ann Internal Med 146(5):317–325, 2007. 10.7326/0003-4819-146-5-200703060-00004; PANAS, Watson et al. in J Person Soc Psychol 47:1063–1070, 1988; Perceived Stress, ad hoc), and post-traumatic growth (PTGI-SF; Cann et al. in Anxiety Stress Coping 23(2):127–137, 2010. 10.1080/10615800903094273), four times throughout the 3 months of the confinement. Linear mixed models showed that the scores on positive functioning variables worsened from the beginning of the confinement, while emotional distress and personal strength improved by the end of the state of alarm. Clustering analyses revealed four different patterns of psychological response: “Survival”, “Resurgent”, “Resilient”, and “Thriving” individuals. Four different profiles were identified during mandatory confinement and most participants remained in the same cluster. The “Resilient” cluster gathered the largest number of individuals (30–37%). We conclude that both the heterogeneity of psychological profiles and analysis of positive functioning variables, emotional distress, and post-traumatic growth must be considered to better understand the response to prolonged adverse situations

Machine learning for mortality analysis in patients with COVID-19

This paper analyzes a sample of patients hospitalized with COVID-19 in the region of Madrid (Spain). Survival analysis, logistic regression, and machine learning techniques (both supervised and unsupervised) are applied to carry out the analysis where the endpoint variable is the reason for hospital discharge (home or deceased). The different methods applied show the importance of variables such as age, O2 saturation at Emergency Rooms (ER), and whether the patient comes from a nursing home. In addition, biclustering is used to globally analyze the patient-drug dataset, extracting segments of patients. We highlight the validity of the classifiers developed to predict the mortality, reaching an appreciable accuracy. Finally, interpretable decision rules for estimating the risk of mortality of patients can be obtained from the decision tree, which can be crucial in the prioritization of medical care and resources.This research was funded by Agencia Estatal de Investigación AEI/FEDER Spain, Project PGC2018-095895-B-I00, and Comunidad Autónoma de Madrid, Spain, Project S2017/BMD-368

Automatic artifact recognition and correction for electrodermal activity based on LSTM-CNN models

[EN] Researchers increasingly use electrodermal activity (EDA) to assess emotional states, developing novel appli-cations that include disorder recognition, adaptive therapy, and mental health monitoring systems. However, movement can produce major artifacts that affect EDA signals, especially in uncontrolled environments where users can freely walk and move their hands. This work develops a fully automatic pipeline for recognizing and correcting motion EDA artifacts, exploring the suitability of long short-term memory (LSTM) and convolutional neural networks (CNN). First, we constructed the EDABE dataset, collecting 74h EDA signals from 43 subjects collected during an immersive virtual reality task and manually corrected by two experts to provide a ground truth. The LSTM-1D CNN model produces the best performance recognizing 72% of artifacts with 88% accuracy, outperforming two state-of-the-art methods in sensitivity, AUC and kappa, in the test set. Subsequently, we developed a polynomial regression model to correct the detected artifacts automatically. Evaluation of the complete pipeline demonstrates that the automatically and manually corrected signals do not present differences in the phasic components, supporting their use in place of expert manual correction. In addition, the EDABE dataset represents the first public benchmark to compare the performance of EDA correction models. This work provides a pipeline to automatically correct EDA artifacts that can be used in uncontrolled conditions. This tool will allow to development of intelligent devices that recognize human emotional states without human intervention.This work was supported by the European Commission [RHUMBO H2020-MSCA-ITN-2018-813234] ; the Generalitat Valenciana, Spain [REBRAND PROMETEU/2019/105] ; the MCIN/AEI, Spain [PID2021-127946OB-I00] ; and the Universitat Politecnica de Valencia, Spain [PAID-10-20].Llanes-Jurado, J.; Lucia A. Carrasco-Ribelles; Alcañiz Raya, ML.; Soria-Olivas, E.; Marín-Morales, J. (2023). Automatic artifact recognition and correction for electrodermal activity based on LSTM-CNN models. Expert Systems with Applications. 230. https://doi.org/10.1016/j.eswa.2023.12058123

La agenda building de los partidos políticos españoles en las redes sociales : Un análisis de Big data

Las redes sociales irradian un flujo de información de opinión horizontal y multimodal que ha abierto nuevos horizontes al análisis de los fenómenos políticos. La ac- ción agregada de los ciudadanos en este nuevo ámbito permite orientar la agenda política del país e influir en la toma de decisiones públicas. El conocimiento de lo que ocurre en las redes deviene, pues, un objeto de estudio de primer interés. En este trabajo se utilizan metodolo- gías sobre plataformas Big Data para analizar la evolución temporal de la agenda política marcada por los partidos políticos en las redes sociales, alguna de cuyas dimen- siones quedan descritas mediante técnicas apropiadas de visualización. En la comunicación, que asume el mar- co teórico y metodológico del comportamentismo, se abordan, en primer lugar, las aproximaciones teóricas al fenómeno de la agenda setting o agenda building y las im- plicaciones que la aparición de las redes sociales digitales ha tenido en este terreno; en segundo lugar, las fuentes teóricas y procedimientos metodológicos de los análisis realizados en plataformas Big Data; y, en tercer lugar, un estudio empírico centrado en el estudio del uso de las re- des por parte de algunos partidos políticos relevante de nuestro país, de sus militantes y de sus simpatizantes. Los resultados del estudio y su descripción a través de nubes de palabras permite hacerse una idea cabal sobre la ima- gen diferenciada que los partidos trasladan, consciente o inconscientemente, a los ciudadanos y ciudadanas que participan en la red y de la importancia que adquiere su control en su actividad diaria

Physical Activity Monitoring and Acceptance of a Commercial Activity Tracker in Adult Patients with Haemophilia

Physical activity (PA) is highly beneficial for people with haemophilia (PWH), however, studies that objectively monitor the PA in this population are scarce. This study aimed to monitor the daily PA and analyse its evolution over time in a cohort of PWH using a commercial activity tracker. In addition, this work analyses the relationship between PA levels, demographics, and joint health status, as well as the acceptance and adherence to the activity tracker. Twenty-six PWH were asked to wear a Fitbit Charge HR for 13 weeks. According to the steps/day in the first week, data were divided into two groups: Active Group (AG; ≥10,000 steps/day) and Non-Active Group (NAG; 0.05) in PA levels or adherence to wristband were produced. Only the correlation between very active minutes and arthropathy was significant (r = −0.40, p = 0.045). Results of the questionnaire showed a high level of satisfaction. In summary, PWH are able to comply with the PA recommendations, and the Fitbit wristband is a valid tool for a continuous and long-term monitoring of PA. However, by itself, the use of a wristband is not enough motivation to increase PA levels

Approaching sales forecasting using recurrent neural networks and transformers

Accurate and fast demand forecast is one of the hot topics in supply chain for enabling the precise execution of the corresponding downstream processes (inbound and outbound planning, inventory placement, network planning, etc). We develop three alternatives to tackle the problem of forecasting the customer sales at day/store/item level using deep learning techniques and the Corporaci\'on Favorita data set, published as part of a Kaggle competition. Our empirical results show how good performance can be achieved by using a simple sequence to sequence architecture with minimal data preprocessing effort. Additionally, we describe a training trick for making the model more time independent and hence improving generalization over time. The proposed solution achieves a RMSLE of around 0.54, which is competitive with other more specific solutions to the problem proposed in the Kaggle competition.Comment: Accepted for publication in Expert Systems and Application

A machine learning approach to identify groups of patients with hematological malignant disorders

Background and Objective: Vaccination against SARS-CoV-2 in immunocompromised patients with hematologic malignancies (HM) is crucial to reduce the severity of COVID-19. Despite vaccination efforts, over a third of HM patients remain unresponsive, increasing their risk of severe breakthrough infections. This study aims to leverage machine learning's adaptability to COVID-19 dynamics, efficiently selecting patient-specific features to enhance predictions and improve healthcare strategies. Highlighting the complex COVID-hematology connection, the focus is on interpretable machine learning to provide valuable insights to clinicians and biologists. Methods: The study evaluated a dataset with 1166 patients with hematological diseases. The output was the achievement or non-achievement of a serological response after full COVID-19 vaccination. Various machine learning methods were applied, with the best model selected based on metrics such as the Area Under the Curve (AUC), Sensitivity, Specificity, and Matthew Correlation Coefficient (MCC). Individual SHAP values were obtained for the best model, and Principal Component Analysis (PCA) was applied to these values. The patient profiles were then analyzed within identified clusters. Results: Support vector machine (SVM) emerged as the best-performing model. PCA applied to SVM-derived SHAP values resulted in four perfectly separated clusters. These clusters are characterized by the proportion of patients that generate antibodies (PPGA). Cluster 1, with the second-highest PPGA (69.91%), included patients with aggressive diseases and factors contributing to increased immunodeficiency. Cluster 2 had the lowest PPGA (33.3%), but the small sample size limited conclusive findings. Cluster 3, representing the majority of the population, exhibited a high rate of antibody generation (84.39%) and a better prognosis compared to cluster 1. Cluster 4, with a PPGA of 66.33%, included patients with B-cell non-Hodgkin's lymphoma on corticosteroid therapy. Conclusions: The methodology successfully identified four separate patient clusters using Machine Learning and Explainable AI (XAI). We then analyzed each cluster based on the percentage of HM patients who generated antibodies after COVID-19 vaccination. The study suggests the methodology's potential applicability to other diseases, highlighting the importance of interpretable ML in healthcare research and decision-makingPID2021-122347NB-I00, PID2021-127946OB-I0

Análisis del efecto del ejercicio físico en la homogeneidad espacial del espectro de la señal de fibrilación ventricular

El presente trabajo estudia las modificaciones intrínsecas que el ejercicio físico produce en la respuesta cardíaca durante fibrilación ventricular (FV), tanto en condiciones de perfusión estable como cuando se produce isquemia en una zona del miocardio. Se estudiarán dichas modificaciones comparándolas con las producidas por el efecto de un fármaco (Glibenclamida) y con un grupo control. El análisis se realizará desde el punto de vista del dominio frecuencial, estudiando la homogeneidad espacial de la frecuencia dominante (ROIsaFD) y de la energía normalizada (ROIsaEN), en registros de cartografía cardíaca unipolar de corazón aislado de conejo. Se utilizarán tres grupos de conejos: control (GC: sin entrenamiento, N=18), entrenados (GE: N=9) y fármaco (GF: sin entrenamiento, con tratamiento, N=15). Se realizarán comparaciones intergrupos, teniendo en cuenta el hecho de que se realizan medidas repetidas en el tiempo, y se harán comparaciones intragrupos para estudiar el efecto del tiempo. Los resultados obtenidos sugieren que los efectos intrínsecos producidos por ejercicio físico sobre la homogeneidad espacial de la respuesta espectral de la FV son similares a los producidos por el tratamiento con Glibenclamida, especialmente cuando se tienen zonas isquémicas

Clasificación de registros de mapeado cardíaco en fibrilación ventricular

El presente trabajo estudia las modificaciones intrínsecas que el ejercicio físico produce en la respuesta cardíaca durante fibrilación ventricular (FV). Para ello se plantea el desarrollo de clasificadores (RL; regresión logística y ELM; Extreme Learning Machine) que diferencien entre el grupo control y los sujetos entrenados. Como parámetros de entrada a los clasificadores se han considerado dos relacionados con el espectro de la señal (FD: frecuencia dominante, y EN: energía normalizada), y otros relacionados con la regularidad y organización de las ondas de activación local, OAL, (IR: índice de regularidad y NO: número de ocurrencias). Se ha realizado un análisis de regiones de interés (ROI) de los tres primeros parámetros para valorar su uniformidad espacial. El trabajo tiene un doble objetivo: estudiar las capacidades de los distintos clasificadores y obtener información acerca de la importancia de las variables a la hora de realizar la clasificación. Se analizaron registros de mapeado cardíaco correspondientes a dos grupos: control (G1: sin entrenamiento, N=10) y entrenados (G2, N=9). Del estudio de las capacidades de ambos clasificadores, se puede observar cómo la ELM obtiene mejores índices de funcionamiento que la RL. Si se analiza el producto sensibilidad por especificidad en el conjunto de validación, se obtiene un 60.73% con la RL y un 72.37% con la ELM. En cuanto al análisis de variables, los resultados obtenidos sugieren que los cambios intrínsecos en FV debidos al ejercicio físico están relacionados con la regularidad morfológica y la uniformidad espectral de las señales de activación del tejido cardíaco