Diagnóstico no invasivo de patologías humanas combinando análisis de aliento y modelización con redes neuronales

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Ciencias Químicas, leída el 09-09-2016It is currently known that there is a direct relation between the moment a disease is detected or diagnosed and the consequences it will have on the patient, as an early detection is generally linked to a more favorable outcome. This concept is the basis of the present research, due to the fact that its main goal is the development of mathematical tools based on computational artificial intelligence to safely and non-invasively attain the detection of multiple diseases. To reach these devices, this research has focused on the breath analysis of patients with diverse diseases, using several analytical methodologies to extract the information contained in these samples, and multiple feature selection algorithms and neural networks for data analysis. In the past, it has been shown that there is a correlation between the molecular composition of breath and the clinical status of a human being, proving the existence of volatile biomarkers that can aid in disease detection depending on their presence or amount. During this research, two main types of analytical approaches have been employed to study the gaseous samples, and these were cross-reactive sensor arrays (based on organically functionalized silicon nanowire field-effect transistors (SiNW FETs) or gold nanoparticles (GNPs)) and proton transfer reaction-mass spectrometry (PTR-MS). The cross-reactive sensors analyze the bulk of the breath samples, offering global, fingerprint-like information, whereas PTR-MS quantifies the volatile molecules present in the samples. All of the analytical equipment employed leads to the generation of large amounts of data per sample, forcing the need of a meticulous mathematical analysis to adequately interpret the results. In this work, two fundamental types of mathematical tools were utilized. In first place, a set of five filter-based feature selection algorithms (χ2 (chi2) score, Fisher’s discriminant ratio, Kruskal-Wallis test, Relief-F algorithm, and information gain test) were employed to reduce the amount of independent in the large databases to the ones which contain the greatest discriminative power for a further modeling task. On the other hand, and in relation to mathematical modeling, artificial neural networks (ANNs), algorithms that are categorized as computational artificial intelligence, have been employed. These non-linear tools have been used to locate the relations between the independent variables of a system and the dependent ones to fulfill estimations or classifications. The type of ANN that has been used in this thesis coincides with the one that is more commonly employed in research, which is the supervised multilayer perceptron (MLP), due to its proven ability to create reliable models for many different applications...Actualmente es sabido que existe una relación directa entre el momento en el cual se detecta o diagnostica una enfermedad y las consecuencias que tendrá sobre el paciente, ya que una detección temprana va generalmente ligada a un desarrollo más favorable. Este concepto es el cimiento de la presente investigación, cuyo objetivo fundamental es el desarrollo de herramientas basadas en inteligencia artificial computacional que consigan, mediante medios seguros y no invasivos, la detección de diversas enfermedades. Para alcanzar dichos sistemas, los estudios han sido enfocados en el análisis de muestras de aliento de pacientes de diversas enfermedades, empleando varias técnicas para extraer información, y diversos algoritmos de selección de variables y redes neuronales para el procesamiento matemático. En el pasado, se ha comprobado que hay una correlación entre la composición molecular del aliento y el estado clínico de una persona, evidenciando la existencia de biomarcadores volátiles que pueden ayudar a detectar enfermedades, ya sea por su presencia o por su cantidad. Durante el transcurso de esta investigación, se han empleado esencialmente dos tipos de técnicas analíticas para estudiar las muestras gaseosas, y estas son conjuntos de sensores de reactividad cruzada (basados en transistores de efecto de campo con nanocables de silicio (SiNW FETs) o en nanopartículas de oro (GNPs), ambos funcionalizados con cadenas orgánicas) y equipos de reacción de transferencia de protones con espectrometría de masas (PTR-MS). Los sensores de reactividad cruzada analizan el aliento en su conjunto, extrayéndose información de la muestra global, mientras que usando PTR-MS, se cuantifican las moléculas volátiles presentes en las muestras analizadas. Todas las técnicas empleadas desembocan en la generación de grandes cantidades de datos por muestra, por lo que un análisis matemático exhaustivo es necesario para poder sacar el máximo rendimiento de los estudios. En este trabajo, se emplearon principalmente dos tipos de herramientas matemáticas. Las primeras son un grupo de cinco algoritmos de selección de variables, concretamente, filtros de variables (cálculos basados en estadística de χ2 (chi2), ratio discriminante de Fisher, análisis de Kruskal-Wallis, algoritmo relief-F y test de ganancia de información), que se han empleado en las bases de datos con grandes cantidades de variables independientes para localizar aquellas con mayor importancia o poder discriminativo para una tarea de modelización matemática posterior. Por otro lado, en cuando a dicha modelización, se ha empleado un tipo de algoritmo que se cataloga dentro del área de la inteligencia artificial computacional: las redes neuronales artificiales (ANNs). Estas herramientas matemáticas de naturaleza no lineal se han utilizado para localizar las relaciones existentes entre las variables independientes de un sistema y las variables dependientes o parámetros a estimar o clasificar. Se ha empleado el tipo de ANN supervisada más extensamente usado en investigación, que son los perceptrones multicapa (MLPs), debido a su habilidad contrastada para originar modelos fiables para numerosas aplicaciones...Fac. de Ciencias QuímicasTRUEunpu

A selected annotated bibliography for spaceborne multiprocessing study

Bibliography on application of multiprocessor systems to space mission

Informatics for devices within telehealth systems for monitoring chronic diseases

Preliminary investigation at the beginning of this research showed that informatics on point-of-care (POC) devices was limited to basic data generation and processing. This thesis is based on publications of several studies during the course of the research. The aim of the research is to model and analyse information generation and exchange in telehealth systems and to identify and analyse the capabilities of these systems in managing chronic diseases which utilise point-of-care devices. The objectives to meet the aim are as follows: (i) to review the state-of-the-art in informatics and decision support on point-of-care devices. (ii) to assess the current level of servitization of POC devices used within the home environment. (iii) to identify current models of information generation and exchange for POC devices using a telehealth perspective. (iv) to identify the capabilities of telehealth systems. (v) to evaluate key components of telehealth systems (i.e. POC devices and intermediate devices). (vi) to analyse the capabilities of telehealth systems as enablers to a healthcare policy. The literature review showed that data transfer from devices is an important part of generating information. The implication of this is that future designs of devices should have efficient ways of transferring data to minimise the errors that may be introduced through manual data entry/transfer. The full impact of a servitized model for point-of-care devices is possible within a telehealth system, since capabilities of interpreting data for the patient will be offered as a service (c.f. NHS Direct). This research helped to deduce components of telehealth systems which are important in supporting informatics and decision making for actors of the system. These included actors and devices. Telehealth systems also help facilitate the exchange of data to help decision making to be faster for all actors concerned. This research has shown that a large number of capability categories existed for the patients and health professionals. There were no capabilities related to the caregiver that had a direct impact on the patient and health professional. This was not surprising since the numbers of caregivers in current telehealth systems was low. Two types of intermediate devices were identified in telehealth systems: generic and proprietary. Patients and caregivers used both types, while health professionals only used generic devices. However, there was a higher incidence of proprietary devices used by patients. Proprietary devices possess features to support patients better thus promoting their independence in managing their chronic condition. This research developed a six-step methodology for working from government objectives to appropriate telehealth capability categories. This helped to determine objectives for which a telehealth system is suitable

Real Time Fault Detection and Diagnostics Using FPGA-Based Architecture

Errors within circuits caused by radiation continue to be an important concern to developers. A new methodology of real time fault detection and diagnostics utilizing FPGA based architectures while under radiation were investigated in this research. The contributions of this research are focused on three areas; a full test platform to evaluate a circuit while under irradiation, an algorithm to detect and diagnose fault locations within a circuit, and finally to characterize Triple Design Triple Modular Redundancy (TDTMR), a new form of TMR. Five different test setups, injected fault test, gamma radiation test, thermal radiation test, optical laser test, and optical flash test, were used to assess the effectiveness of these three research goals. The testing platform was constructed with two FPGA boards, the Device Under Test (DUT) and the controller board, to generate and evaluate specific vector sets sent to the DUT. The testing platform combines a myriad of testing and measuring equipment and work hours onto one small reprogrammable and reusable FPGA. This device was able to be used in multiple test setups. The controlling logic can be interchanged to test multiple circuit designs under various forms of radiation. The detection and diagnostic algorithm was designed to determine fault locations in real time. The algorithm used for diagnosing the fault location uses inverse deductive elimination. By using test generation tools, fault lists were developed. The fault lists were used to narrow \ the possible fault locations within the circuit. The algorithm is able to detect single stuck at faults based on these lists. The algorithm can also detect multiple output errors but not able to diagnose multiple stuck at faults in real time

Dagstuhl Reports : Volume 1, Issue 2, February 2011

Online Privacy: Towards Informational Self-Determination on the Internet (Dagstuhl Perspectives Workshop 11061) : Simone Fischer-Hübner, Chris Hoofnagle, Kai Rannenberg, Michael Waidner, Ioannis Krontiris and Michael Marhöfer Self-Repairing Programs (Dagstuhl Seminar 11062) : Mauro Pezzé, Martin C. Rinard, Westley Weimer and Andreas Zeller Theory and Applications of Graph Searching Problems (Dagstuhl Seminar 11071) : Fedor V. Fomin, Pierre Fraigniaud, Stephan Kreutzer and Dimitrios M. Thilikos Combinatorial and Algorithmic Aspects of Sequence Processing (Dagstuhl Seminar 11081) : Maxime Crochemore, Lila Kari, Mehryar Mohri and Dirk Nowotka Packing and Scheduling Algorithms for Information and Communication Services (Dagstuhl Seminar 11091) Klaus Jansen, Claire Mathieu, Hadas Shachnai and Neal E. Youn

A two-level structure for advanced space power system automation

The tasks to be carried out during the three-year project period are: (1) performing extensive simulation using existing mathematical models to build a specific knowledge base of the operating characteristics of space power systems; (2) carrying out the necessary basic research on hierarchical control structures, real-time quantitative algorithms, and decision-theoretic procedures; (3) developing a two-level automation scheme for fault detection and diagnosis, maintenance and restoration scheduling, and load management; and (4) testing and demonstration. The outlines of the proposed system structure that served as a master plan for this project, work accomplished, concluding remarks, and ideas for future work are also addressed

Physical Intelligent Sensors

This paper proposes the development of intelligent sensors as part of an integrated systems approach, i.e. one treats the sensors as a complete system with its own sensing hardware (the traditional sensor), A/D converters, processing and storage capabilities, software drivers, self-assessment algorithms, communication protocols and evolutionary methodologies that allow them to get better with time. Under a project being undertaken at the NASA s Stennis Space Center, an integrated framework is being developed for the intelligent monitoring of smart elements. These smart elements can be sensors, actuators or other devices. The immediate application is the monitoring of the rocket test stands, but the technology should be generally applicable to the Integrated Systems Health Monitoring (ISHM) vision. This paper outlines progress made in the development of intelligent sensors by describing the work done till date on Physical Intelligent Sensors (PIS). The PIS discussed here consists of a thermocouple used to read temperature in an analog form which is then converted into digital values. A microprocessor collects the sensor readings and runs numerous embedded event detection routines on the collected data and if any event is detected, it is reported, stored and sent to a remote system through an Ethernet connection. Hence the output of the PIS is data coupled with confidence factor in the reliability of the data which leads to information on the health of the sensor at all times. All protocols are consistent with IEEE 1451.X standards. This work lays the foundation for the next generation of smart devices that have embedded intelligence for distributed decision making capabilities

Principle of Duality on Prognostics

The accurate estimation of the remaining useful life (RUL) of various components and devices used in complex systems, e.g., airplanes remain to be addressed by scientists and engineers. Currently, there area wide range of innovative proposals put forward that intend on solving this problem. Integrated System Health Management (ISHM) has thus far seen some growth in this sector, as a result of the extensive progress shown in demonstrating feasible and viable techniques. The problems related to these techniques were that they often consumed time and were too expensive and resourceful to develop. In this paper we present a radically novel approach for building prognostic models that compensates and improves on the current prognostic models inconsistencies and problems. Broadly speaking, the new approach proposes a state of the art technique that utilizes the physics of a system rather than the physics of a component to develop its prognostic model. A positive aspect of this approach is that the prognostic model can be generalized such that a new system could be developed on the basis and principles of the prognostic model of another system. This paper will mainly explore single switch dc-to-dc converters which will be used as an experiment to exemplify the potential success that can be discovered from the development of a novel prognostic model that can efficiently estimate the remaining useful life of one system based on the prognostics of its dual system

USSR Space Life Sciences Digest, issue 31

This is the thirty first issue of NASA's Space Life Sciences Digest. It contains abstracts of 55 journal papers or book chapters published in Russian and of 5 Soviet monographs. Selected abstracts are illustrated with figures and tables from the original. The abstracts in this issue have been identified as relevant to 18 areas of space biology and medicine. These areas include: adaptation, biological rhythms, cardiovascular and respiratory systems, endocrinology, enzymology, genetics, group dynamics, habitability and environmental effects, hematology, life support systems, metabolism, microbiology, musculoskeletal system, neurophysiology, nutrition, operational medicine, psychology, radiobiology, and space biology and medicine