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...

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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
id UNACIONAL2_4d2856194c07ed0e2bdf178feb70b5f3
oai_identifier_str oai:repositorio.unal.edu.co:unal/19997
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network_name_str Universidad Nacional de Colombia
repository_id_str
spelling 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
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dc.type.content.spa.fl_str_mv Text
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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
dc.format.mimetype.spa.fl_str_mv application/pdf
institution Universidad Nacional de Colombia
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repository.name.fl_str_mv Repositorio Institucional Universidad Nacional de Colombia
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