Cardiovascular information for improving biometric recognition

Mención Internacional en el título de doctorThe improvements of the last two decades in data modeling and computing have lead to new biometric modalities. The Electrocardiogram (ECG) modality is part of them, and has been mainly researched by using public databases related to medical training. Despite of being useful for initial approaches, they are not representative of a real biometric environment. In addition, publishing and creating a new database is none trivial due to human resources and data protection laws. The main goal of this thesis is to successfully use ECG as a biometric signal while getting closer to the real case scenario. Every experiment considers low computational calculations and transformations to help in potential portability. The core experiments in this work come from a private database with different positions, time and heart rate scenarios. An initial segmentation evaluation is achieved with the help of fiducial point detection which determines the QRS selection as the input data for all the experiments. The approach of training a model per user (open-set) is tested with different machine learning algorithms, only getting an acceptable result with Gaussian Mixture Models (GMM). However, the concept of training all users in one model (closed-set) shows more potential with Linear Discriminant Analysis (LDA), whose results were improved in 40%. The results with LDA are also tested as a multi-modality technique, decreasing the Equal Error Rate (EER) of fingerprint verification in up to 70.64% with score fusion, and reaching 0% in Protection Attack Detection (PAD). The Multilayer Perceptron (MLP) algorithm enhances these results in verification while applying the first differentiation to the signal. The network optimization is achieved with EER as an observation metric, and improves the results of LDA in 22% for the worst case scenario, and decreases the EER to 0% in the best case. Complexity is added creating a Convolutional Neural Network (CNN) and Long-Short Term Memory (LSTM) based network, BioECG. The tuning process is achieved without extra feature transformation and is evaluated through accuracy, aiming for good identification. The inclusion of a second day of enrollment in improves results from MLP, reaching the overall lowest results of 0.009%–1.352% in EER. Throughout the use of good quality signals, position changes did not noticeably impact the verification. In addition, collecting data in a different day or in a different hour did not clearly affect the performance. Moreover, modifying the verification process based on attempts, improves the overall results, up to reach a 0% EER when applying BioECG. Finally, to get closer to a real scenario, a smartband prototype is used to collect new databases. A private database with limited scenarios but controlled data, and another local database with a wider range of scenarios and days, and with a more relaxed use of the device. Applying the concepts of first differentiation and MLP, these signals required the Stationary Wavelet Transform (SWT) and new fiducial point detection to improve their results. The first database gave subtle chances of being used in identification with up to 78.2% accuracy, but the latter was completely discarded for this purpose. These realistic experiments show the impact of a low fidelity sensor, even considering the same modifications in previous successful experiments with better quality data, reaching up to 13.530% EER. In the second database, results reach a range of 0.068%–31.669% EER. This type of sensor is affected by heart rate changes, but also by position variations, given its sensitivity to movement.Las mejoras en modelado de datos y computación de las últimas dos décadas, han llevado a la creación de nuevas modalidades biométricas. La modalidad de electrocardiograma (ECG) es una de ellas, la cual se ha investigado usando bases de datos públicas que fueron creadas para entrenamiento de profesional médico. Aunque estos datos han sido útiles para los estados iniciales de la modalidad, no son representativos de un entorno biométrico real. Además, publicar y crear bases de datos nuevas son problemas no triviales debido a los recursos humanos y las leyes de protección de datos. El principal objetivo de esta tesis es usar exitosamente datos de ECG como señales biométricas a la vez que nos acercamos a un escenario realista. Cada experimento considera cálculos y transformadas de bajo coste computacional para ayudar en su potencial uso en aparatos móviles. Los principales experimentos de este trabajo se producen con una base de datos privada con diferentes escenarios en términos de postura, tiempo y frecuencia cardíaca. Con ella se evalúan las diferentes seleccións del complejo QRS mediante detección de puntos fiduciales, lo cual servirá como datos de entrada para el resto de experimentos. El enfoque de entrenar un modelo por usuario (open-set) se prueba con diferentes algoritmos de aprendizaje máquina (machine learning), obteniendo resultados aceptables únicamente mediante el uso de modelos de mezcla de Gaussianas (Gaussian Mixture Models, GMM). Sin embargo, el concepto de entrenar un modelo con todos los usuarios (closed-set) demuestra mayor potencial con Linear Discriminant Analysis (Análisis de Discriminante Lineal, LDA), cuyos resultados mejoran en un 40%. Los resultados de LDA también se utilizan como técnica multi-modal, disminuyendo la Equal Error Rate (Tasa de Igual Error, EER) de la verificación mediante huella en hasta un 70.64% con fusión de puntuación, y llegando a un sistema con un 0% de EER en Detección de Ataques de Presentación (Presentation Attack Detection, PAD). El algoritmo de Perceptrón Multicapa (Multilayer Perceptron, MLP) mejora los resultados previos en verificación aplicando la primera derivada a la señal. La optimización de la red se consigue en base a su EER, mejora la de LDA en hasta un 22% en el peor caso, y la lleva hasta un 0% en el mejor caso. Se añade complejidad creando una red neural convolucional (Convolutional Neural Network, CNN) con una red de memoria a largo-corto plazo (Long-Short Term Memory, LSTM), llamada BioECG. El proceso de ajuste de hiperparámetros se lleva acabo sin transformaciones y se evalúa observando la accuracy (precisión), para mejorar la identificación. Sin embargo, incluir un segundo día de registro (enrollment) con BioECG, estos resultados mejoran hasta un 74% para el peor caso, llegando a los resultados más bajos hasta el momento con 0.009%–1.352% en la EER. Durante el uso de señales de buena calidad, los cambios de postura no afectaron notablemente a la verificación. Además, adquirir los datos en días u horas diferentes tampoco afectó claramente a los resultados. Asimismo, modificar el proceso de verificación en base a intentos también produce mejoría en todos los resultados, hasta el punto de llegar a un 0% de EER cuando se aplica BioECG. Finalmente, para acercarnos al caso más realista, se usa un prototipo de pulsera para capturar nuevas bases de datos. Una base de datos privada con escenarios limitados pero datos más controlados, y otra base de datos local con más espectro de escenarios y días y un uso del dispositivo más relajado. Para estos datos se aplican los conceptos de primera diferenciación en MLP, cuyas señales requieren la Transformada de Wavelet Estacionaria (Stationary Wavelet Transform, SWT) y un detector de puntos fiduciales para mejorar los resultados. La primera base de datos da opciones a ser usada para identificación con un máximo de precisión del 78.2%, pero la segunda se descartó completamente para este propósito. Estos experimentos más realistas demuestran el impact de tener un sensor de baja fidelidad, incluso considerando las mismas modificaciones que previamente tuvieron buenos resultados en datos mejores, llegando a un 13.530% de EER. En la segunda base de datos, los resultados llegan a un rango de 0.068%–31.669% en EER. Este tipo de sensor se ve afectado por las variaciones de frecuencia cardíaca, pero también por el cambio de posición, dado que es más sensible al movimiento.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidente: Cristina Conde Vilda.- Secretario: Mariano López García.- Vocal: Young-Bin Know

Extracción de descriptores de movimiento en vídeos para la evaluación de la estética

The growth of video streaming has increased noticeably through the last decade. Because of it, the task of searching and recommending videos is becoming more and more difficult. Whereas before the existence of video streaming information retrievals was only based on text and metadata, nowadays content-based image and video retrieval is starting to be researched. In order to add value and success to user’s searchings, it is interesting to assess the quality and aesthetic value of the information it is retrieved. On this thesis we are going to extract several motion related descriptors in order to aesthetically assess a car commercial database. The videos in the database are extracted from YouTube and labeled in order to metadata provided by the website. Specifically three kinds of labeling are going to be used: based on quality or likes/- dislikes, quantity or number of views and the combination of both of them. Quality and quantity provide a binary labeling, and the combination clusters the videos in four classes. As it is usually done in computer vision, the main objective is suggesting a set of descriptors and designing and providing the procedures for calculating their values on the corpus of videos. These values are called descriptors, and they can be obtained by processing frames and handling the data got on the procedure to get specific numbers. With their help it may be possible to know whether they give enough information to determine the aesthetic appealing of the videos. On this project we are going focus on motion descriptors. As an approach to get data about the video motion, the optical flow is estimated between each pair of frames. To do so, a Matlab friendly C++ code developed is used. This algorithm is based on the brightness constancy assumption between two frames, leading to a continuous spatial-temporal function. This is discretized, linearized and the temporal factor removed by assuming only the function in two frames. Afterwards the zero gradient values are found using Iterative Reweighted Least Squares (IRLS), a method which iterates calculating different weights in order to find the ones fulfilling the zero gradient condition. From this a linear system is obtained and it is solved by using Successive Over-Relaxation (SOR) method, which is Gauss’ variant with faster convergence. The optical flow algorithm needs several parameters to be set. Because of the difficulty of setting these parameters automatically, these values are determined by the observation of each performance and efficiency representing the observed motion. When the optical flow is calculated, we filter homogeneous texture regions, due to possible error estimation induced by similar pixel values on the neighborhood. In order to determine the texture level on different frame regions, we measure the entropy on each one, which will provide a measurement of pixel’s randomness. This is made by turning each frame into gray-scale and dividing them into 60 different windows. Afterwards, a threshold is set to determine which region will be considered as a low texture one. This is done considering that filtering excessively could mean that the descriptors extraction will not be representative. However, in cases with a lot of very homogeneous regions (e.g.: completely black) the amount of vectors discarded will be high no matter what threshold is set. Then, when the region’s entropy is less than the threshold, it will be considered as a low texture one and, as a consequence, its optical flow vectors will not be taken into consideration. After filtering based on the texture, the first step is getting the angle and modulus of the movement estimated in every pixel using the components got. For easy direction interpretation when getting the different descriptors, the angles are nominalized according to the 8 cardinal points. By using both cardinals and modulus obtained, it is possible to estimate approximately which camera motion is taking place on every frame or shot. For this we are only taking into account those values on the margins of each frame. Previous to the camera motion detection, it is necessary to apply some weights to the cardinal values on the margins. This is done not only to give relevance to N-S-E-W about the camera motion although they do not belong to the purest pan and tilt motion types. Adding each different weight depending on the cardinal, we get a percentage in comparison to the ideal motion type (this is, every pixel moving towards the same direction) which gives out the “amount” of movement going to each N-S-E-W direction. The most common shot type on the database is done by using fixed cameras and it is detected by setting a threshold to the mean modulus of the margins of each frame, which should include those frames which are fixed but have some kind of movement on the margins because of the captured scene. If it is less than the mentioned threshold, the frame will be considered as fixed. If not, we begin to detect zoom presence. In order to do that, margins are divided into 2 vertical and 2 horizontal regions, and each maximum percentage cardinal is obtained. When detecting which type of zoom it is, we know the specific directions each margin should have in theory. Starting from that, we can compare the theoretical value with the maximum direction got before using weights. When 3 or 4 of the margin’s directions correspond to the theoretical pixels motion in each zoom type, the current frame will be considered as one with zoom in or out, depending on the conditions. Considering the zoom just under 3 conditions is not that restrictive, and this is because having just those 3 conditions fulfilled is very unlikely unless we have a zoom. If the zoom is not the case, we evaluate whether it is a pan or tilt camera motion. Now, the maximum percentage is obtained among all the margins instead of dividing them into regions, because directions should be the same along all the frame. If the difference of the maximum value with respect to the rest of the cardinal percentages is greater than a threshold, and the maximum value is higher than another different threshold, it will be considered pan right/left or tilt up/down depending on the cardinal which belongs to the maximum. This is done in order to discard those maximums in non predominant directions. Finally, if none of these conditions are fulfilled, the frame will have assigned a non-specific motion. Once frame camera motion is determined, we proceed to detect shots on each video by computing the Sum of Absolute Differences (SAD) of the gray intensity pixels and its first and second derivatives. Shot changes are detected when this second derivative has a value greater than a chosen threshold. After shot detection, the mode camera motion type is obtained, as well as the percentage of frames on the shot with that value. When this percentage is greater than a threshold, it is considered the shot has a predominant value which corresponds to the most present motion on the frames. At this point the data computed are not at video level. This is why we need to use the data in order to obtain single values which represent each video. In order to get statistical parameters at video level, it is not possible to iterate creating a matrix with every single angle or modulus value, because it is highly memory and computing time consuming, so it is necessary to do it sequentially. This means getting single values on each frame which will be helpful when computing statistical video descriptors. As angles have a circular nature, using circular statistics is compulsory. For this purpose we are going to store only the sum of each vector component through the whole set of frames, as well as the sum of modulus. We also obtain the number of pixels taken into account on the operation because it will not be constant due to the low texture region filtering. With these values we get everything needed to compute the mean an standard deviation at video level. However, when dealing with data like camera motion type through frames and shots, it is not possible to get means and standard deviations, and this is why we get the percentage of each motion type at shot and frame level. When data handling is done, we extract 27 different descriptors which are going to be evaluated using the three labeling methods previously referred. By using machine learning algorithms provided by Weka, several features and classifiers are tested, getting the best performance using quantity labeling and angle and modulus related features, getting a 60% accuracy with tree classifier SimpleCart. Although in general descriptors performance is not remarkably good, by using the Experimenter tool provided by Weka, we can find out which set of features and classifiers really provide a statistically significant improvement with respect to ZeroR classifier. We observe that with combination labeling the accuracy is less than those in quality and quantity labeling. This is because combination deals binary, although it does not mean that combination labeling works worse than the binary ones, because its improvement with respect to ZeroR could be better. In fact, combination labeling gives more information because it has an statistically relevant performance when choosing angles and modulus related features and SimpleCart and SimpleLogistic classifiers, which means that the accuracy percentage is not something casual. We also get significant results when using quantity labeling and the same set of features with SimpleCart. These results lead to the conclusion that camera motion is not particularly relevant when assessing aesthetics on this database. This is something contradictory to what one might think, because camera motion is used typically to add drama on an audiovisual context. An explanation to this could be the fact that in general, fixed and hand-carried camera motion are noticeably common on the database and that is why it does not really affect when the user decides whether he likes it or not. In addition, it is well known that establishing ground-truth when dealing with people’s likings is not trivial because of their subjectivity, and this could affect results. The lack of ca database with camera motion labels is also crucial, because it makes difficult knowing if the rest of the non-manually labeled videos behave correctly when the camera motion detection method is applied. In this project we check that not always theoretical knowledge corresponds to what it is observed in a practical context, but we also provide a way to extract descriptors and analyze them with a simple approach. This could be improved in the future by labeling the database with respect to the camera motion and by segmenting background in order to improve the steady camera detection. Binary aesthetic labeling could be also improved by using supervised annotation extracted by measuring involuntary biological responses experienced by the evaluator.En este proyecto se extraen diferentes características relacionadas con el movimiento de los vídeos proporcionados en la base de datos, los cuales se corresponden con anuncios de coches. Para ello, se proporciona una estimación del flujo óptico haciendo uso del algoritmo proporcionado. Mediante un análisis del flujo óptico en los márgenes de los fotogramas se caracteriza el movimiento de cámara presente en éstos y se calculan los ángulos y módulos correspondientes al movimiento de cada píxel. Posteriormente se procede a un cálculo secuencial de estos valores para obtener descriptores a nivel de vídeo. Con los datos obtenidos y con tres diferentes etiquetados de los vídeos basados en calidad, cantidad y en la combinación de éstos, se aplican métodos de aprendizaje máquina con diferentes conjuntos de descriptores y clasificadores para la evaluación de la estética, la cual estará basada en los metadatos proporcionados por los usuarios a través de YouTube. Se concluye de esta manera que el tipo de movimiento de cámara no afecta notablemente a la evaluación estética por parte de los usuarios, mientras que sí lo hacen el ángulo y módulo presentes en cada vídeo.Ingeniería en Tecnologías de Telecomunicació

BioECG: Improving ECG biometrics with deep learning and enhanced datasets

Nowadays, Deep Learning tools have been widely applied in biometrics. Electrocardiogram (ECG) biometrics is not the exception. However, the algorithm performances rely heavily on a representative dataset for training. ECGs suffer constant temporal variations, and it is even more relevant to collect databases that can represent these conditions. Nonetheless, the restriction in database publications obstructs further research on this topic. This work was developed with the help of a database that represents potential scenarios in biometric recognition as data was acquired in different days, physical activities and positions. The classification was implemented with a Deep Learning network, BioECG, avoiding complex and time-consuming signal transformations. An exhaustive tuning was completed including variations in enrollment length, improving ECG verification for more complex and realistic biometric conditions. Finally, this work studied one-day and two-days enrollments and their effects. Two-days enrollments resulted in huge general improvements even when verification was accomplished with more unstable signals. EER was improved in 63% when including a change of position, up to almost 99% when visits were in a different day and up to 91% if the user experienced a heartbeat increase after exercise

Unsupervised and scalable low train pathology detection system based on neural networks

Currently, there exist different technologies applied in the world of medicine dedicated to the detection of health problems such as cancer, heart diseases, etc. However, these technologies are not applied to the detection of lower body pathologies. In this article, a Neural Network (NN)-based system capable of classifying pathologies of the lower train by the way of walking in a non-controlled scenario, with the ability to add new users without retraining the system is presented. All the signals are filtered and processed in order to extract the Gait Cycles (GCs), and those cycles are used as input for the NN. To optimize the network a random search optimization process has been performed. To test the system a database with 51 users and 3 visits per user has been collected. After some improvements, the algorithm can correctly classify the 92% of the cases with 60% of training data. This algorithm is a first approach of creating a system to make a first stage pathology detection without the requirement to move to a specific place

QRS Differentiation to Improve ECG Biometrics under Different Physical Scenarios Using Multilayer Perceptron

This article belongs to the Special Issue Electrocardiogram (ECG) Signal and Its Applications.Currently, machine learning techniques are successfully applied in biometrics and Electrocardiogram (ECG) biometrics specifically. However, not many works deal with different physiological states in the user, which can provide significant heart rate variations, being these a key matter when working with ECG biometrics. Techniques in machine learning simplify the feature extraction process, where sometimes it can be reduced to a fixed segmentation. The applied database includes visits taken in two different days and three different conditions (sitting down, standing up after exercise), which is not common in current public databases. These characteristics allow studying differences among users under different scenarios, which may affect the pattern in the acquired data. Multilayer Perceptron (MLP) is used as a classifier to form a baseline, as it has a simple structure that has provided good results in the state-of-the-art. This work studies its behavior in ECG verification by using QRS complexes, finding its best hyperparameter configuration through tuning. The final performance is calculated considering different visits for enrolling and verification. Differentiation in the QRS complexes is also tested, as it is already required for detection, proving that applying a simple first differentiation gives a good result in comparison to state-of-the-art similar works. Moreover, it also improves the computational cost by avoiding complex transformations and using only one type of signal. When applying different numbers of complexes, the best results are obtained when 100 and 187 complexes in enrolment, obtaining Equal Error Rates (EER) that range between 2.79–4.95% and 2.69–4.71%, respectively

Implementación de la metodología flipped classroom en los laboratorios de Química Analítica

Adaptar el sistema tradicional de aprendizaje a las necesidades actuales del alumnado empleando la metodología flipped classroom en el laboratorio de Química Analítica I, con el objetivo de fomentar el aprendizaje utilizando herramientas digitales.Depto. de Química AnalíticaFac. de Ciencias QuímicasFALSEsubmitte

¿Qué queda de mí?

Este libro es una reclamación a quienes hemos sido, somos o seremos docentes. A quienes no hemos respetado a las personas que se han puesto junto a nosotros y nosotras, confiando su bien más preciado: la libertad. Estas páginas denuncian cada vez que convertimos una visión en la visión, una emoción en la emoción, un saber en el saber, un comportamiento en el comportamiento. Es un grito contra la imposición, la normalización, la neutralización y la universalización de una perspectiva particular. Una pugna contra cada proceso que no se ha conectado con las vidas de los aprendices. Un texto colaborativo realizado por alumnado de Educación y Cambio Social en el Grado en Educación Infantil de la Universidad de Málaga y coordinado por Ignacio Calderón Almendros

TGF-β-induced IGFBP-3 is a key paracrine factor from activated pericytes that promotes colorectal cancer cell migration and invasion.

The crosstalk between cancer cells and the tumor microenvironment has been implicated in cancer progression and metastasis. Fibroblasts and immune cells are widely known to be attracted to and modified by cancer cells. However, the role of pericytes in the tumor microenvironment beyond endothelium stabilization is poorly understood. Here, we report that pericytes promoted colorectal cancer (CRC) cell proliferation, migration, invasion, stemness, and chemoresistance in vitro, as well as tumor growth in a xenograft CRC model. We demonstrate that coculture with human CRC cells induced broad transcriptomic changes in pericytes, mostly associated with TGF-β receptor activation. The prognostic value of a TGF-β response signature in pericytes was analyzed in CRC patient data sets. This signature was found to be a good predictor of CRC relapse. Moreover, in response to stimulation by CRC cells, pericytes expressed high levels of TGF-β1, initiating an autocrine activation loop. Investigation of secreted mediators and underlying molecular mechanisms revealed that IGFBP-3 is a key paracrine factor from activated pericytes affecting CRC cell migration and invasion. In summary, we demonstrate that the interplay between pericytes and CRC cells triggers a vicious cycle that stimulates pericyte cytokine secretion, in turn increasing CRC cell tumorigenic properties. Overall, we provide another example of how cancer cells can manipulate the tumor microenvironment.This study was funded by grants from Instituto de Salud Carlos III (PI13/00090, PI16/00357), partially supported by the European Regional Development Fund (ERDF), Comunidad de Madrid (S2010‐BMD‐2312), and Ministerio de Economía y Competitividad (RTC‐2016‐5118‐1 and SAF2017‐89437‐P), cofinanced by Programa Estatal de Investigación and the European Union. AT‐G and LM‐G was supported by Comunidad Autónoma de Madrid/European Social Fund (PEJ16/MED/AI‐1961, PEJ‐2018‐PRE/BMD‐8314, and PEJ‐2018‐TL/BMD‐11483).S

TGF‐β‐induced IGFBP‐3 is a key paracrine factor from activated pericytes that promotes colorectal cancer cell migration and invasion

The crosstalk between cancer cells and the tumor microenvironment has been implicated in cancer progression and metastasis. Fibroblasts and immune cells are widely known to be attracted to and modified by cancer cells. However, the role of pericytes in the tumor microenvironment beyond endothelium stabilization is poorly understood. Here, we report that pericytes promoted colorectal cancer (CRC) cell proliferation, migration, invasion, stemness, and chemoresistance in vitro, as well as tumor growth in a xenograft CRC model. We demonstrate that coculture with human CRC cells induced broad transcriptomic changes in pericytes, mostly associated with TGF‐β receptor activation. The prognostic value of a TGF‐β response signature in pericytes was analyzed in CRC patient data sets. This signature was found to be a good predictor of CRC relapse. Moreover, in response to stimulation by CRC cells, pericytes expressed high levels of TGF‐β1, initiating an autocrine activation loop. Investigation of secreted mediators and underlying molecular mechanisms revealed that IGFBP‐3 is a key paracrine factor from activated pericytes affecting CRC cell migration and invasion. In summary, we demonstrate that the interplay between pericytes and CRC cells triggers a vicious cycle that stimulates pericyte cytokine secretion, in turn increasing CRC cell tumorigenic properties. Overall, we provide another example of how cancer cells can manipulate the tumor microenvironment