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Aplicaciones de redes neuronales artificiales a estudios neurofisiológicos en neuropatías periféricas focales
dc.contributor.advisor | Almario, Diego Fernando | spa |
dc.contributor.author | Gutiérrez G., Jorge Eduardo | spa |
dc.contributor.author | Peña Paz, Lyda | spa |
dc.date.accessioned | 2020-06-26T21:32:15Z | |
dc.date.available | 2020-06-26T21:32:15Z | |
dc.date.issued | 2005 | |
dc.identifier.uri | http://hdl.handle.net/20.500.12749/3303 | |
dc.description.abstract | Introducción: La interpretación de estudios electrofisiológicos es esencialmente una tarea de clasificación. Las redes neuronales artificiales (ANN) son herramientas adecuadas para la clasificación porque son basado en técnicas de reconocimiento de patrones. Objetivos: Desarrollar un sistema informático para la detección automatizada de neuropatías focales. utilizando ANN. Métodos: El estudio se basó en 300 conjuntos de estudios de conducción nerviosa (NCS) de tres diferentes laboratorios de medicina de electrodiagnóstico. Cada conjunto de datos de entrada estaba formado por 11 parámetros, incluyendo latencias motoras y sensoriales, amplitudes, duraciones y velocidades de un solo nervio. Los conjuntos de entrada se clasificaron en 4 subgrupos de neuropatía focal (distal desmielinización, desmielinización proximal, desmielinización generalizada, pérdida de axones) según sobre el tipo de daño nervioso más 1 adicional para hallazgos normales. Los datos fueron presentados a una ANN de retropropagación con 1 capa oculta. La estructura de la red se modificó para lograr el error cuadrático medio más bajo posible. Los resultados de estas redes de primer nivel se presentaron a una red de segundo nivel para detectar neuropatías generalizadas. Después entrenando a las ANN, la precisión de la clasificación se probó utilizando otro conjunto de datos que se desconocido para las redes. Resultados: Se alcanzó una precisión de clasificación del 99% para la detección de patologías patrones. La precisión para la clasificación de neuropatías focales fue del 95,2%. Conclusiones: las redes neuronales clasifican subgrupos de neuropatía focal con alta precisión (> 95%). Este método puede conducir a la detección automática de neuropatía focal. | spa |
dc.description.sponsorship | Instituto Tecnológico de Estudios Superiores de Monterrey ITESM | spa |
dc.description.tableofcontents | SUMMARY 11 INTRODUCCION 1 1 EL PROBLEMA 3 1.1 DESCRIPCIÓN DEL PROBLEMA 3 1.2 FORMULACIÓN DEL PROBLEMA 5 1.3 OBJETIVO GENERAL 5 1.4 OBJETIVOS ESPECIFICOS 5 1.5 JUSTIFICACIÓN 6 1.6 ALCANCES Y LIMITACIONES 7 2 MARCO DE REFERENCIA 9 2.1 ANTECEDENTES DE LA INVESTIGACIÓN 9 2.2 MARCO TEÓRICO CONCEPTUAL 11 2.2.1 Medicina Electrodiagnóstica 12 2.2.2 Inteligencia Artificial y Medicina 45 2.2.3 Redes Neuronales Artificiales 61 2.2.4 Aplicaciones de redes neuronales a Medicina 94 2.2.5 Aplicaciones de redes neuronales a electrodiagnóstico 104 3 METODOLOGÍA 106 3.1 DATOS 106 3.1.1 Salidas deseadas 106 3.1.2 Selección de los datos de entrada 107 3.1.3 Preprocesamiento de los datos de entrada 109 3.1.4 Datos Faltantes 110 3.1.5 Fuente de los datos 111 3.2 ARQUITECTURA DE LA RED 113 3.2.1 Tipo de red 114 3.2.2 Mejorar la Generalización 115 3.2.3 Arquitectura de la Red 1 116 3.2.4 Arquitectura de la Red 2 121 3.3 SOFTWARE 124 3.4 HARDWARE 125 3.5 ENTRENAMIENTO 125 3.6 VALIDACIÓN DE LA RED 126 4 RESULTADOS 127 4.1 RED 1 (ESTRUCTURA DE RED GENERAL) 127 4.2 RED 2 (RED NERVIO MEDIANO) 128 4.3 RED 3 (RED NERVIO ULNAR) 130 4.4 RED 4 (RED DE GENERALIZACIÓN) 132 4.5 VALIDACIÓN DE RESULTADOS 135 5 DISCUSIÓN 137 CONCLUSIONES 139 RECOMENDACIONES 141 BIBLIOGRAFIA 142 REFERENCIAS ELECTRONICAS | spa |
dc.format.mimetype | application/pdf | spa |
dc.language.iso | spa | spa |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/2.5/co/ | * |
dc.title | Aplicaciones de redes neuronales artificiales a estudios neurofisiológicos en neuropatías periféricas focales | spa |
dc.title.translated | Applications of artificial neural networks to neurophysiological studies in focal peripheral neuropathies | eng |
dc.degree.name | Magíster en Ciencias Computacionales | spa |
dc.coverage | Bucaramanga (Colombia) | spa |
dc.publisher.grantor | Universidad Autónoma de Bucaramanga UNAB | spa |
dc.rights.local | Abierto (Texto Completo) | spa |
dc.publisher.faculty | Facultad Ingeniería | spa |
dc.publisher.program | Maestría en Ciencias Computacionales | spa |
dc.description.degreelevel | Maestría | spa |
dc.type.driver | info:eu-repo/semantics/masterThesis | |
dc.type.local | Tesis | spa |
dc.type.coar | http://purl.org/coar/resource_type/c_bdcc | |
dc.subject.keywords | Artificial neural networks (Computers) | eng |
dc.subject.keywords | Neuropathy | eng |
dc.subject.keywords | Computer science | eng |
dc.subject.keywords | Diseases | eng |
dc.subject.keywords | Diagnosis | eng |
dc.subject.keywords | Data processing | eng |
dc.subject.keywords | Investigations | eng |
dc.subject.keywords | Analysis | eng |
dc.subject.keywords | Systems engineering | eng |
dc.subject.keywords | Focal neuropathy | eng |
dc.subject.keywords | Automated detection | eng |
dc.identifier.instname | instname:Universidad Autónoma de Bucaramanga - UNAB | spa |
dc.identifier.reponame | reponame:Repositorio Institucional UNAB | spa |
dc.type.hasversion | info:eu-repo/semantics/acceptedVersion | |
dc.rights.accessrights | info:eu-repo/semantics/openAccess | spa |
dc.rights.accessrights | http://purl.org/coar/access_right/c_abf2 | spa |
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dc.contributor.cvlac | https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000196606 | * |
dc.subject.lemb | Redes neuronales artificiales (Computadores) | spa |
dc.subject.lemb | Neuropatía | spa |
dc.subject.lemb | Ingeniería de sistemas | spa |
dc.subject.lemb | Ciencias computacionales | spa |
dc.subject.lemb | Enfermedades | spa |
dc.subject.lemb | Diagnóstico | spa |
dc.subject.lemb | Procesamiento de datos | spa |
dc.subject.lemb | Investigaciones | spa |
dc.subject.lemb | Análisis | spa |
dc.contributor.corporatename | Instituto Tecnológico y de Estudios Superiores de Monterrey (ITESM) | spa |
dc.description.abstractenglish | Introduction: Interpreting electrophysiological studies is essentially a classification task. Artificial neural networks (ANNs) are suitable tools for classification because they are based on pattern recognition techniques. Objectives: To develop a computer system for automated detection of focal neuropathies using ANNs. Methods: The study was based on 300 sets of nerve conduction studies (NCSs) from three different electrodiagnostic medicine laboratories. Each input data set was formed by 11 parameters, including motor and sensory latencies, amplitudes, durations, and velocities of a single nerve. The input sets were classified into 4 focal neuropathy subgroups (distal demyelination, proximal demyelination, generalized demyelination, axon loss) depending on the type of nerve damage plus 1 additional for normal findings. The data were presented to a backpropagation ANN with 1 hidden layer. The network structure was modified to achieve the lowest possible mean square error. The outputs from these first-level networks were presented to a second-level network in order to detect generalized neuropathies. After training the ANNs, the classification accuracy was tested using another data set that was unknown to the networks. Results: A classification accuracy of 99% was reached for the detection of pathologic patterns. The accuracy for focal neuropathies classification was 95.2%.Conclusions: Neural networks classify focal neuropathy subgroups with high accuracy (>95%). This method may lead to automated focal neuropathy detection. | eng |
dc.subject.proposal | Neuropatía focal | |
dc.subject.proposal | Detección automatizada | |
dc.type.redcol | http://purl.org/redcol/resource_type/TM | |
dc.rights.creativecommons | Atribución-NoComercial-SinDerivadas 2.5 Colombia | * |
dc.coverage.campus | UNAB Campus Bucaramanga | spa |
dc.description.learningmodality | Modalidad Presencial | spa |