Arquitectura abierta encalable para monitorización domiciliaria: aplicación a pacientes con patologías cardiacas

En esta Tesis se propone una arquitectura abierta y escalable destinada a lamonitorizaci´on domiciliaria.Con el aumento de la poblaci´on y la mejora de la calidad y esperanza devida, la ocupaci´on de los hospitales es un problema cada d´ýa m´as acuciante.Desde hace tiempo, se viene pensando como posible soluci´on las consultas ylos cuidados a distancia, lo cual no es otra cosa que la telemedicina.El auge de las telecomunicaciones y su implantaci´on en la sociedad modernahacen viable su uso en medicina, y se presentan como una soluci´on id´oneaal problema citado.En esta Tesis se plantea el uso de tecnolog´ýas y herramientas modernas paraaportar soluciones al problema de la monitorizaci´on de pacientes remota,a trav´es de una arquitectura modular que permite implementar servicioseficientes para la telemonitorizaci´on.Se hace un estudio de los sistemas de monitorizaci´on remota existentes hoyen d´ýa, tanto a nivel comercial como a nivel de investigaci´on. Se trata deaportar una soluci´on, basada en t´ecnicas de procesado digital de se nales, queadem´as de crear un simple canal de comunicaci´on aporte una monitorizaci´on"inteligente" que ayude a la toma de decisiones por parte de los especialistas.Se tienen en cuenta diversos aspectos como el ahorro de ancho de banda enlos canales de comunicaci´on, la seguridad en las transferencias y el uso deest´andares para facilitar la integraci´on con los sistemas existentes y futuros.Como ejemplo pr´actico de la arquitectura propuesta, se implementa un sistemade monitorizaci´on remota enfocado a pacientes que sufren alg´un trastornocard´ýaco, fundamentalmente enfocado a pacientes post-infarto. Dichosistema es capaz de analizar la se nal de electrocardiograma por s´ý mismo ygenerar alarmas en caso de que se produzca alg´un peligro. Para completarlo,se implementa tambi´en un modelo de servidor que recoge los datos analizadospor el sistema remoto y permite gestionarlos desde el Centro M´edico.Se han evaluado los m´odulos implementados, y se ha hecho un an´alisis exhaustivode los algoritmos propuestos. Por ´ultimo se han establecido lasconclusiones y se indica la proyecci´on futura para posteriores trabajos.In this PhD thesis, we propose and test an open and scalable arquitecturefor telemonitoring.Occupation in hospitals is a very relevant problem nowadays. A possiblesolution could be the use of teleconsulting and telecare, which essentiallyare telemedicine counterparts.In the last years, telecommunication networks have been improved and in-stalled. Nowadays, it is possible to use them in medicine, and they representa very good solution to our problem.This PhD thesis uses modern technologies and tools in order to o®er so-lutions for telemonitoring. We propose a modular system that allows theimplementation of e±cient services for telemonitoring.A previous study of the state of the art telemonitoring systems has beendone. Both commercial systems and projects of other research groups havebeen studied. Our goal is to ¯nd a solution, based on digital processingtechniques, that serves not only as a communication channel, but as aninteligent system which helps experts to take the correct decisions. For thatpurpose, we have taken into account di®erent aspects such as optimal use ofthe bandwidth, security transfers and standars accomodation of the systemspeci¯cations. They are all closely related to facilitate the integration withother present and future systems.As an example of the proposed arquitecture, we have implemented a tele-monitoring system for patients with cardiac pathologies, focusing mainly inpost-infarcted patients. Our system can analyze the electrocardiogram sig-nal and generate an alarm if it detects any risk situation. To complete thesystem, we have also implemented a server which receives data sent by ourremote device and allows its management from the hospital center.The implemented modules have been evaluated. We have done exhaustivetests of the proposed algorithms. Finally, conclusions have been establishedand directions for future works have been proposed

Structured Output SVM for Remote Sensing Image Classification

Traditional kernel classifiers assume independence among the classification outputs. As a consequence, each misclassification receives the same weight in the loss function. Moreover, the kernel function only takes into account the similarity between input values and ignores possible relationships between the classes to be predicted. These assumptions are not consistent for most of real-life problems. In the particular case of remote sensing data, this is not a good assumption either. Segmentation of images acquired by airborne or satellite sensors is a very active field of research in which one tries to classify a pixel into a predefined set of classes of interest (e.g. water, grass, trees, etc.). In this situation, the classes share strong relationships, e.g. a tree is naturally (and spectrally) more similar to grass than to water. In this paper, we propose a first approach to remote sensing image classification using structured output learning. In our approach, the output space structure is encoded using a hierarchical tree, and these relations are added to the model in both the kernel and the loss function. The methodology gives rise to a set of new tools for structured classification, and generalizes the traditional non-structured classification methods. Comparison to standard SVM is done numerically, statistically and by visual inspection of the obtained classification maps. Good results are obtained in the challenging case of a multispectral image of very high spatial resolution acquired with QuickBird over a urban are

Non-linear System Identification with Composite Relevance Vector Machines

Nonlinear system identification based on relevance vector machines (RVMs) has been traditionally addressed by stacking the input and/or output regressors and then performing standard RVM regression. This letter introduces a full family of composite kernels in order to integrate the input and output information in the mapping function efficiently and hence generalize the standard approach. An improved trade-off between accuracy and sparsity is obtained in several benchmark problems. Also, the RVM yields confidence intervals for the predictions, and it is less sensitive to free parameter selectionPublicad

Learning non-linear time scales with Kernel γ-Filters

A family of kernel methods, based on the γ-filter structure, is presented for non-linear system identification and time series prediction. The kernel trick allows us to develop the natural non-linear extension of the (linear) support vector machine (SVM) γ-filter, but this approach yields a rigid system model without non-linear cross relation between time-scales. Several functional analysis properties allow us to develop a full, principled family of kernel γ-filters. The improved performance in several application examples suggests that a more appropriate representation of signal states is achieved.Publicad

Kernel-Based Framework for Multitemporal and Multisource Remote Sensing Data Classification and Change Detection

The multitemporal classification of remote sensing images is a challenging problem, in which the efficient combination of different sources of information (e.g., temporal, contextual, or multisensor) can improve the results. In this paper, we present a general framework based on kernel methods for the integration of heterogeneous sources of information. Using the theoretical principles in this framework, three main contributions are presented. First, a novel family of kernel-based methods for multitemporal classification of remote sensing images is presented. The second contribution is the development of nonlinear kernel classifiers for the well-known difference and ratioing change detection methods by formulating them in an adequate high-dimensional feature space. Finally, the presented methodology allows the integration of contextual information and multisensor images with different levels of nonlinear sophistication. The binary support vector (SV) classifier and the one-class SV domain description classifier are evaluated by using both linear and nonlinear kernel functions. Good performance on synthetic and real multitemporal classification scenarios illustrates the generalization of the framework and the capabilities of the proposed algorithms.Publicad