Análisis Dinámico de Relevancia en Bioseñales
Abstract : In this work, a methodology for biosignal analysis (e.g. pathology diagnosis) is discussed, which is based on dynamic relevance analysis of stochastic features extracted from different decomposition techniques of biosignal recordings. Dimension reduction is carried out by adapting in time...
- Autores:
-
Sepúlveda Cano, Lina María
- Tipo de recurso:
- Doctoral thesis
- Fecha de publicación:
- 2013
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/19997
- Acceso en línea:
- https://repositorio.unal.edu.co/handle/unal/19997
http://bdigital.unal.edu.co/10221/
- Palabra clave:
- 0 Generalidades / Computer science, information and general works
51 Matemáticas / Mathematics
61 Ciencias médicas; Medicina / Medicine and health
Análisis de bioseñales
procesos estocásticos
sistemas de reconocimiento de configuraciones
biosignal analysis
stochastic processes
Pattern recognition systems
- Rights
- openAccess
- License
- Atribución-NoComercial 4.0 Internacional
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Universidad Nacional de Colombia |
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Atribución-NoComercial 4.0 InternacionalDerechos reservados - Universidad Nacional de Colombiahttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Castellanos Domínguez, César GermánSepúlveda Cano, Lina Maríaa59c273c-2652-42fc-aaed-6cf5e050dcc43002019-06-25T18:22:10Z2019-06-25T18:22:10Z2013https://repositorio.unal.edu.co/handle/unal/19997http://bdigital.unal.edu.co/10221/Abstract : In this work, a methodology for biosignal analysis (e.g. pathology diagnosis) is discussed, which is based on dynamic relevance analysis of stochastic features extracted from different decomposition techniques of biosignal recordings. Dimension reduction is carried out by adapting in time commonly used latent variable techniques, in such a way, that the data information is maximally preserved for a given relevance function. Specifically, since the maximum variance is assumed as a measure of relevance, time– adapted supervised approaches are developed. Additionally, in the case of high dimensionality data with significant correlation among the whole set, a dimensionality reduction technique is proposed, based on time–frequency relevance maps. The proposed approaches are experimentally assessed on real-world data sets, allowing to confirm whether the proposed feature selection algorithm is adequate for classification purposes. The conjunction of these advances conforms a methodology for training pattern recognition systems, which is a fully automatized dimensionality reduction method that allows the use of functional representations. The main advantage of the proposed methodology, is that preserves the maximum information among the high dimensional input data. In this terms of classifi- cation performance, the proposed methodology is efficient and competitive, outperforming other similar methods.Doctoradoapplication/pdfspaUniversidad Nacional de Colombia Sede Manizales Facultad de Ingeniería y Arquitectura Departamento de Ingeniería Eléctrica, Electrónica y ComputaciónDepartamento de Ingeniería Eléctrica, Electrónica y ComputaciónSepúlveda Cano, Lina María (2013) Análisis Dinámico de Relevancia en Bioseñales. Doctorado thesis, Universidad Nacional de Colombia - Sede Manizales.0 Generalidades / Computer science, information and general works51 Matemáticas / Mathematics61 Ciencias médicas; Medicina / Medicine and healthAnálisis de bioseñalesprocesos estocásticossistemas de reconocimiento de configuracionesbiosignal analysisstochastic processesPattern recognition systemsAnálisis Dinámico de Relevancia en BioseñalesTrabajo de grado - Doctoradoinfo:eu-repo/semantics/doctoralThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_db06Texthttp://purl.org/redcol/resource_type/TDORIGINAL7910003.2013.pdfapplication/pdf2211529https://repositorio.unal.edu.co/bitstream/unal/19997/1/7910003.2013.pdfcca1e5684223944f408e1ce5b4d304b5MD51THUMBNAIL7910003.2013.pdf.jpg7910003.2013.pdf.jpgGenerated Thumbnailimage/jpeg3857https://repositorio.unal.edu.co/bitstream/unal/19997/2/7910003.2013.pdf.jpgbca03b66ecd05ed3906c01e055b8e92cMD52unal/19997oai:repositorio.unal.edu.co:unal/199972023-09-24 23:07:03.676Repositorio Institucional Universidad Nacional de Colombiarepositorio_nal@unal.edu.co |
dc.title.spa.fl_str_mv |
Análisis Dinámico de Relevancia en Bioseñales |
title |
Análisis Dinámico de Relevancia en Bioseñales |
spellingShingle |
Análisis Dinámico de Relevancia en Bioseñales 0 Generalidades / Computer science, information and general works 51 Matemáticas / Mathematics 61 Ciencias médicas; Medicina / Medicine and health Análisis de bioseñales procesos estocásticos sistemas de reconocimiento de configuraciones biosignal analysis stochastic processes Pattern recognition systems |
title_short |
Análisis Dinámico de Relevancia en Bioseñales |
title_full |
Análisis Dinámico de Relevancia en Bioseñales |
title_fullStr |
Análisis Dinámico de Relevancia en Bioseñales |
title_full_unstemmed |
Análisis Dinámico de Relevancia en Bioseñales |
title_sort |
Análisis Dinámico de Relevancia en Bioseñales |
dc.creator.fl_str_mv |
Sepúlveda Cano, Lina María |
dc.contributor.author.spa.fl_str_mv |
Sepúlveda Cano, Lina María |
dc.contributor.spa.fl_str_mv |
Castellanos Domínguez, César Germán |
dc.subject.ddc.spa.fl_str_mv |
0 Generalidades / Computer science, information and general works 51 Matemáticas / Mathematics 61 Ciencias médicas; Medicina / Medicine and health |
topic |
0 Generalidades / Computer science, information and general works 51 Matemáticas / Mathematics 61 Ciencias médicas; Medicina / Medicine and health Análisis de bioseñales procesos estocásticos sistemas de reconocimiento de configuraciones biosignal analysis stochastic processes Pattern recognition systems |
dc.subject.proposal.spa.fl_str_mv |
Análisis de bioseñales procesos estocásticos sistemas de reconocimiento de configuraciones biosignal analysis stochastic processes Pattern recognition systems |
description |
Abstract : In this work, a methodology for biosignal analysis (e.g. pathology diagnosis) is discussed, which is based on dynamic relevance analysis of stochastic features extracted from different decomposition techniques of biosignal recordings. Dimension reduction is carried out by adapting in time commonly used latent variable techniques, in such a way, that the data information is maximally preserved for a given relevance function. Specifically, since the maximum variance is assumed as a measure of relevance, time– adapted supervised approaches are developed. Additionally, in the case of high dimensionality data with significant correlation among the whole set, a dimensionality reduction technique is proposed, based on time–frequency relevance maps. The proposed approaches are experimentally assessed on real-world data sets, allowing to confirm whether the proposed feature selection algorithm is adequate for classification purposes. The conjunction of these advances conforms a methodology for training pattern recognition systems, which is a fully automatized dimensionality reduction method that allows the use of functional representations. The main advantage of the proposed methodology, is that preserves the maximum information among the high dimensional input data. In this terms of classifi- cation performance, the proposed methodology is efficient and competitive, outperforming other similar methods. |
publishDate |
2013 |
dc.date.issued.spa.fl_str_mv |
2013 |
dc.date.accessioned.spa.fl_str_mv |
2019-06-25T18:22:10Z |
dc.date.available.spa.fl_str_mv |
2019-06-25T18:22:10Z |
dc.type.spa.fl_str_mv |
Trabajo de grado - Doctorado |
dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/doctoralThesis |
dc.type.version.spa.fl_str_mv |
info:eu-repo/semantics/acceptedVersion |
dc.type.coar.spa.fl_str_mv |
http://purl.org/coar/resource_type/c_db06 |
dc.type.content.spa.fl_str_mv |
Text |
dc.type.redcol.spa.fl_str_mv |
http://purl.org/redcol/resource_type/TD |
format |
http://purl.org/coar/resource_type/c_db06 |
status_str |
acceptedVersion |
dc.identifier.uri.none.fl_str_mv |
https://repositorio.unal.edu.co/handle/unal/19997 |
dc.identifier.eprints.spa.fl_str_mv |
http://bdigital.unal.edu.co/10221/ |
url |
https://repositorio.unal.edu.co/handle/unal/19997 http://bdigital.unal.edu.co/10221/ |
dc.language.iso.spa.fl_str_mv |
spa |
language |
spa |
dc.relation.ispartof.spa.fl_str_mv |
Universidad Nacional de Colombia Sede Manizales Facultad de Ingeniería y Arquitectura Departamento de Ingeniería Eléctrica, Electrónica y Computación Departamento de Ingeniería Eléctrica, Electrónica y Computación |
dc.relation.references.spa.fl_str_mv |
Sepúlveda Cano, Lina María (2013) Análisis Dinámico de Relevancia en Bioseñales. Doctorado thesis, Universidad Nacional de Colombia - Sede Manizales. |
dc.rights.spa.fl_str_mv |
Derechos reservados - Universidad Nacional de Colombia |
dc.rights.coar.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
dc.rights.license.spa.fl_str_mv |
Atribución-NoComercial 4.0 Internacional |
dc.rights.uri.spa.fl_str_mv |
http://creativecommons.org/licenses/by-nc/4.0/ |
dc.rights.accessrights.spa.fl_str_mv |
info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Atribución-NoComercial 4.0 Internacional Derechos reservados - Universidad Nacional de Colombia http://creativecommons.org/licenses/by-nc/4.0/ http://purl.org/coar/access_right/c_abf2 |
eu_rights_str_mv |
openAccess |
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application/pdf |
institution |
Universidad Nacional de Colombia |
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Repositorio Institucional Universidad Nacional de Colombia |
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