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dc.rights.licenseAtribución-NoComercial 4.0 Internacional
dc.contributorCastellanos Domínguez, César Germán
dc.contributor.authorSepúlveda Cano, Lina María
dc.date.accessioned2019-06-25T18:22:10Z
dc.date.available2019-06-25T18:22:10Z
dc.date.issued2013
dc.identifier.urihttps://repositorio.unal.edu.co/handle/unal/19997
dc.description.abstractAbstract : 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.
dc.format.mimetypeapplication/pdf
dc.language.isospa
dc.relation.ispartofUniversidad Nacional de Colombia Sede Manizales Facultad de Ingeniería y Arquitectura Departamento de Ingeniería Eléctrica, Electrónica y Computación
dc.relation.ispartofDepartamento de Ingeniería Eléctrica, Electrónica y Computación
dc.rightsDerechos reservados - Universidad Nacional de Colombia
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/
dc.subject.ddc0 Generalidades / Computer science, information and general works
dc.subject.ddc51 Matemáticas / Mathematics
dc.subject.ddc61 Ciencias médicas; Medicina / Medicine and health
dc.titleAnálisis Dinámico de Relevancia en Bioseñales
dc.typeTrabajo de grado - Doctorado
dc.type.driverinfo:eu-repo/semantics/doctoralThesis
dc.type.versioninfo:eu-repo/semantics/acceptedVersion
dc.identifier.eprintshttp://bdigital.unal.edu.co/10221/
dc.description.degreelevelDoctorado
dc.relation.referencesSepúlveda Cano, Lina María (2013) Análisis Dinámico de Relevancia en Bioseñales. Doctorado thesis, Universidad Nacional de Colombia - Sede Manizales.
dc.rights.accessrightsinfo:eu-repo/semantics/openAccess
dc.subject.proposalAnálisis de bioseñales
dc.subject.proposalprocesos estocásticos
dc.subject.proposalsistemas de reconocimiento de configuraciones
dc.subject.proposalbiosignal analysis
dc.subject.proposalstochastic processes
dc.subject.proposalPattern recognition systems
dc.type.coarhttp://purl.org/coar/resource_type/c_db06
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.contentText
dc.type.redcolhttp://purl.org/redcol/resource_type/TD
oaire.accessrightshttp://purl.org/coar/access_right/c_abf2


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Atribución-NoComercial 4.0 InternacionalThis work is licensed under a Creative Commons Reconocimiento-NoComercial 4.0.This document has been deposited by the author (s) under the following certificate of deposit