Aplicaciones de redes neuronales artificiales a estudios neurofisiológicos en neuropatías periféricas focales

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 informá...

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Autores:
Gutiérrez G., Jorge Eduardo
Peña Paz, Lyda
Tipo de recurso:
Fecha de publicación:
2005
Institución:
Universidad Autónoma de Bucaramanga - UNAB
Repositorio:
Repositorio UNAB
Idioma:
spa
OAI Identifier:
oai:repository.unab.edu.co:20.500.12749/3303
Acceso en línea:
http://hdl.handle.net/20.500.12749/3303
Palabra clave:
Artificial neural networks (Computers)
Neuropathy
Computer science
Diseases
Diagnosis
Data processing
Investigations
Analysis
Systems engineering
Focal neuropathy
Automated detection
Redes neuronales artificiales (Computadores)
Neuropatía
Ingeniería de sistemas
Ciencias computacionales
Enfermedades
Diagnóstico
Procesamiento de datos
Investigaciones
Análisis
Neuropatía focal
Detección automatizada
Rights
openAccess
License
http://creativecommons.org/licenses/by-nc-nd/2.5/co/
id UNAB2_2e98efd382ab486cd07a629e03917762
oai_identifier_str oai:repository.unab.edu.co:20.500.12749/3303
network_acronym_str UNAB2
network_name_str Repositorio UNAB
repository_id_str
dc.title.spa.fl_str_mv Aplicaciones de redes neuronales artificiales a estudios neurofisiológicos en neuropatías periféricas focales
dc.title.translated.eng.fl_str_mv Applications of artificial neural networks to neurophysiological studies in focal peripheral neuropathies
title Aplicaciones de redes neuronales artificiales a estudios neurofisiológicos en neuropatías periféricas focales
spellingShingle Aplicaciones de redes neuronales artificiales a estudios neurofisiológicos en neuropatías periféricas focales
Artificial neural networks (Computers)
Neuropathy
Computer science
Diseases
Diagnosis
Data processing
Investigations
Analysis
Systems engineering
Focal neuropathy
Automated detection
Redes neuronales artificiales (Computadores)
Neuropatía
Ingeniería de sistemas
Ciencias computacionales
Enfermedades
Diagnóstico
Procesamiento de datos
Investigaciones
Análisis
Neuropatía focal
Detección automatizada
title_short Aplicaciones de redes neuronales artificiales a estudios neurofisiológicos en neuropatías periféricas focales
title_full Aplicaciones de redes neuronales artificiales a estudios neurofisiológicos en neuropatías periféricas focales
title_fullStr Aplicaciones de redes neuronales artificiales a estudios neurofisiológicos en neuropatías periféricas focales
title_full_unstemmed Aplicaciones de redes neuronales artificiales a estudios neurofisiológicos en neuropatías periféricas focales
title_sort Aplicaciones de redes neuronales artificiales a estudios neurofisiológicos en neuropatías periféricas focales
dc.creator.fl_str_mv Gutiérrez G., Jorge Eduardo
Peña Paz, Lyda
dc.contributor.advisor.spa.fl_str_mv Almario, Diego Fernando
dc.contributor.author.spa.fl_str_mv Gutiérrez G., Jorge Eduardo
Peña Paz, Lyda
dc.contributor.cvlac.*.fl_str_mv https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000196606
dc.contributor.corporatename.spa.fl_str_mv Instituto Tecnológico y de Estudios Superiores de Monterrey (ITESM)
dc.subject.keywords.eng.fl_str_mv Artificial neural networks (Computers)
Neuropathy
Computer science
Diseases
Diagnosis
Data processing
Investigations
Analysis
Systems engineering
Focal neuropathy
Automated detection
topic Artificial neural networks (Computers)
Neuropathy
Computer science
Diseases
Diagnosis
Data processing
Investigations
Analysis
Systems engineering
Focal neuropathy
Automated detection
Redes neuronales artificiales (Computadores)
Neuropatía
Ingeniería de sistemas
Ciencias computacionales
Enfermedades
Diagnóstico
Procesamiento de datos
Investigaciones
Análisis
Neuropatía focal
Detección automatizada
dc.subject.lemb.spa.fl_str_mv Redes neuronales artificiales (Computadores)
Neuropatía
Ingeniería de sistemas
Ciencias computacionales
Enfermedades
Diagnóstico
Procesamiento de datos
Investigaciones
Análisis
dc.subject.proposal.none.fl_str_mv Neuropatía focal
Detección automatizada
description 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.
publishDate 2005
dc.date.issued.none.fl_str_mv 2005
dc.date.accessioned.none.fl_str_mv 2020-06-26T21:32:15Z
dc.date.available.none.fl_str_mv 2020-06-26T21:32:15Z
dc.type.driver.none.fl_str_mv info:eu-repo/semantics/masterThesis
dc.type.local.spa.fl_str_mv Tesis
dc.type.redcol.none.fl_str_mv http://purl.org/redcol/resource_type/TM
dc.identifier.uri.none.fl_str_mv http://hdl.handle.net/20.500.12749/3303
dc.identifier.instname.spa.fl_str_mv instname:Universidad Autónoma de Bucaramanga - UNAB
dc.identifier.reponame.spa.fl_str_mv reponame:Repositorio Institucional UNAB
url http://hdl.handle.net/20.500.12749/3303
identifier_str_mv instname:Universidad Autónoma de Bucaramanga - UNAB
reponame:Repositorio Institucional UNAB
dc.language.iso.spa.fl_str_mv spa
language spa
dc.relation.references.spa.fl_str_mv Gutiérrez G., Jorge Eduardo, Peña Paz, Lyda (2005). Aplicaciones de redes neurales artificiales a estudios neurofisiológicos en neuropatías periféricas focales. Bucaramanga (Colombia) : Universidad Autónoma de Bucaramanga UNAB, Instituto Tecnológico y de Estudios Superiores de Monterrey ITESM
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dc.coverage.spa.fl_str_mv Bucaramanga (Colombia)
dc.coverage.campus.spa.fl_str_mv UNAB Campus Bucaramanga
dc.publisher.grantor.spa.fl_str_mv Universidad Autónoma de Bucaramanga UNAB
dc.publisher.faculty.spa.fl_str_mv Facultad Ingeniería
dc.publisher.program.spa.fl_str_mv Maestría en Ciencias Computacionales
institution Universidad Autónoma de Bucaramanga - UNAB
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spelling Almario, Diego Fernando25415d68-e348-47fd-8145-f4f46267b2b3Gutiérrez G., Jorge Eduardocaaae854-7482-49d0-bfc9-e7d4387dec4dPeña Paz, Lydad1a55277-7da2-4356-aee9-069d904e3ec0https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000196606Instituto Tecnológico y de Estudios Superiores de Monterrey (ITESM)2020-06-26T21:32:15Z2020-06-26T21:32:15Z2005http://hdl.handle.net/20.500.12749/3303instname:Universidad Autónoma de Bucaramanga - UNABreponame:Repositorio Institucional UNABIntroducción: 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.Instituto Tecnológico de Estudios Superiores de Monterrey ITESMSUMMARY 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 ELECTRONICASMaestríaIntroduction: 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.Modalidad Presencialapplication/pdfspahttp://creativecommons.org/licenses/by-nc-nd/2.5/co/Abierto (Texto Completo)info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Atribución-NoComercial-SinDerivadas 2.5 ColombiaAplicaciones de redes neuronales artificiales a estudios neurofisiológicos en neuropatías periféricas focalesApplications of artificial neural networks to neurophysiological studies in focal peripheral neuropathiesMagíster en Ciencias ComputacionalesBucaramanga (Colombia)UNAB Campus BucaramangaUniversidad Autónoma de Bucaramanga UNABFacultad IngenieríaMaestría en Ciencias Computacionalesinfo:eu-repo/semantics/masterThesisTesishttp://purl.org/redcol/resource_type/TMArtificial neural networks (Computers)NeuropathyComputer scienceDiseasesDiagnosisData processingInvestigationsAnalysisSystems engineeringFocal neuropathyAutomated detectionRedes neuronales artificiales (Computadores)NeuropatíaIngeniería de sistemasCiencias computacionalesEnfermedadesDiagnósticoProcesamiento de datosInvestigacionesAnálisisNeuropatía focalDetección automatizadaGutiérrez G., Jorge Eduardo, Peña Paz, Lyda (2005). 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Proceedings of the 13rd IEEE Symposium on Computer-based Medical Systems CBMS’00, Houston TX. 2000.ORIGINAL2005_Tesis_Jorge_Eduardo_Gutierrez.pdf2005_Tesis_Jorge_Eduardo_Gutierrez.pdfTesisapplication/pdf1333845https://repository.unab.edu.co/bitstream/20.500.12749/3303/1/2005_Tesis_Jorge_Eduardo_Gutierrez.pdfbcae47bab84e86521bccf75625ff44c8MD51open accessTHUMBNAIL2005_Tesis_Jorge_Eduardo_Gutierrez.pdf.jpg2005_Tesis_Jorge_Eduardo_Gutierrez.pdf.jpgIM Thumbnailimage/jpeg5025https://repository.unab.edu.co/bitstream/20.500.12749/3303/2/2005_Tesis_Jorge_Eduardo_Gutierrez.pdf.jpg8ad7228156abbbfbbb788dc5b0ce93c5MD52open access20.500.12749/3303oai:repository.unab.edu.co:20.500.12749/33032023-07-27 10:44:17.892open accessRepositorio Institucional | Universidad Autónoma de Bucaramanga - UNABrepositorio@unab.edu.co

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