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dc.contributor.authorLópez de Ipiña Peña, Miren Karmele
dc.contributor.authorAlonso, Jesús Bernardino
dc.contributor.authorTravieso, Carlos Manuel
dc.contributor.authorSole-Casals, Jordi
dc.contributor.authorEguiraun Martínez, Harkaitz
dc.contributor.authorFaúndez Zanuy, Marcos
dc.contributor.authorEzeiza Ramos, Aitzol
dc.contributor.authorBarroso Moreno, Nora
dc.contributor.authorEcay Torres, Miriam
dc.contributor.authorMartínez-Lage Alvarez, Pablo
dc.contributor.authorMartínez de Lizarduy Sturtze, Unai
dc.date.accessioned2014-02-04T16:52:31Z
dc.date.available2014-02-04T16:52:31Z
dc.date.issued2013-05
dc.identifier.citationSensors 13(5) : 6730-6745 (2013)es
dc.identifier.issn1424-8220
dc.identifier.urihttp://hdl.handle.net/10810/11337
dc.description.abstractThe work presented here is part of a larger study to identify novel technologies and biomarkers for early Alzheimer disease (AD) detection and it focuses on evaluating the suitability of a new approach for early AD diagnosis by non-invasive methods. The purpose is to examine in a pilot study the potential of applying intelligent algorithms to speech features obtained from suspected patients in order to contribute to the improvement of diagnosis of AD and its degree of severity. In this sense, Artificial Neural Networks (ANN) have been used for the automatic classification of the two classes (AD and control subjects). Two human issues have been analyzed for feature selection: Spontaneous Speech and Emotional Response. Not only linear features but also non-linear ones, such as Fractal Dimension, have been explored. The approach is non invasive, low cost and without any side effects. Obtained experimental results were very satisfactory and promising for early diagnosis and classification of AD patients.es
dc.description.sponsorshiphis work has been partially supported by a SAIOTEK from the Basque Government, University of Vic under the research grant R0904, and the Spanish Ministerio de Ciencia e Innovacion TEC2012-38630-C04-03. Iciar Martinez (Research Center for Experimental Marine Biology and Biotechnology-Plentziako Itsas Estazioa (PIE), University of the Basque Country & IKERBASQUE, Basque Foundation for Sciencees
dc.language.isoenges
dc.publisherMDPIes
dc.relationinfo:eu-repo/grantAgreement/MINECO/TEC2012-38630-C04-03
dc.rightsinfo:eu-repo/semantics/openAccesses
dc.subjectAlzheimer's disease diagnosises
dc.subjectspontaneous speeches
dc.subjectemotion recognition;es
dc.subjectmachine learninges
dc.subjectnon-invasive diagnostic techniqueses
dc.subjectdementiaes
dc.titleOn the Selection of Non-Invasive Methods Based on Speech Analysis Oriented to Automatic Alzheimer Disease Diagnosises
dc.typeinfo:eu-repo/semantics/articlees
dc.rights.holder© 2013 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license http://creativecommons.org/licenses/by/3.0/)es
dc.relation.publisherversionhttp://www.mdpi.com/1424-8220/13/5/6730es
dc.identifier.doi10.3390/s130506730
dc.departamentoesIngeniería de sistemas y automáticaes_ES
dc.departamentoeuSistemen ingeniaritza eta automatikaes_ES
dc.subject.categoriaBIOCHEMISTRY AND MOLECULAR BIOLOGY
dc.subject.categoriaPHYSICS, ATOMIC, MOLECULAR AND CHEMICAL
dc.subject.categoriaELECTRICAL AND ELECTRONIC ENGINEERING
dc.subject.categoriaCHEMISTRY, ANALYTICAL


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