Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/44018
DC FieldValueLanguage
dc.contributor.authorLópez-de-Ipiña, Karmeleen_US
dc.contributor.authorAlonso, Jesus-Bernardinoen_US
dc.contributor.authorManuel Travieso, Carlosen_US
dc.contributor.authorSolé-Casals, Jordien_US
dc.contributor.authorEgiraun, Harkaitzen_US
dc.contributor.authorFaundez-Zanuy, Marcosen_US
dc.contributor.authorEzeiza, Aitzolen_US
dc.contributor.authorBarroso, Noraen_US
dc.contributor.authorEcay-Torres, Miriamen_US
dc.contributor.authorMartinez-Lage, Pabloen_US
dc.contributor.authorDe Lizardui, Unai Martinezen_US
dc.contributor.otherLopez-de-Ipina, Karmele-
dc.contributor.otherEguiraun, Harkaitz-
dc.contributor.otherSole-Casals, Jordi-
dc.contributor.otherEcay-Torres, Mirian-
dc.contributor.otherMartinez de Lizarduy Sturtze, Unai-
dc.contributor.otherBarroso, Nora-
dc.contributor.otherAlonso-Hernandez, Jesus B.-
dc.contributor.otherFaundez-Zanuy, Marcos-
dc.contributor.otherTravieso-Gonzalez, Carlos M.-
dc.contributor.otherEzeiza, Aitzol-
dc.date.accessioned2018-11-21T19:36:30Z-
dc.date.available2018-11-21T19:36:30Z-
dc.date.issued2013en_US
dc.identifier.issn1424-8220en_US
dc.identifier.urihttp://hdl.handle.net/10553/44018-
dc.description.abstractAbstract: The 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.en_US
dc.languagespaen_US
dc.publisher1424-8220-
dc.relation.ispartofSensorsen_US
dc.sourceSensors[ISSN 1424-8220],v. 13 (5), p. 6730-6745en_US
dc.subject3307 Tecnología electrónicaen_US
dc.subject.otherAlzheimer’s disease diagnosis; spontaneous speech; emotion recognition; machine learning; non-invasive diagnostic techniques; dementiaen_US
dc.titleOn the selection of non-invasive methods based on speech analysis oriented to automatic Alzheimer disease diagnosisen_US
dc.typeinfo:eu-repo/semantics/Articlees
dc.typeArticlees
dc.identifier.doi10.3390/s130506730
dc.identifier.scopus84879021372-
dc.identifier.isi000319445600072-
dcterms.isPartOfSensors-
dcterms.sourceSensors[ISSN 1424-8220],v. 13 (5), p. 6730-6745-
dc.contributor.authorscopusid56263484400-
dc.contributor.authorscopusid24774957200-
dc.contributor.authorscopusid6602376272-
dc.contributor.authorscopusid14018739300-
dc.contributor.authorscopusid55765445200-
dc.contributor.authorscopusid6701452104-
dc.contributor.authorscopusid14022747600-
dc.contributor.authorscopusid23392059500-
dc.contributor.authorscopusid55765237800-
dc.contributor.authorscopusid6603115791-
dc.contributor.authorscopusid55765340200-
dc.description.lastpage6745-
dc.description.firstpage6730-
dc.relation.volume13-
dc.investigacionIngeniería y Arquitecturaen_US
dc.type2Artículoen_US
dc.identifier.wosWOS:000319445600072-
dc.contributor.daisngid1399740-
dc.contributor.daisngid418703-
dc.contributor.daisngid265761-
dc.contributor.daisngid642260-
dc.contributor.daisngid4627708-
dc.contributor.daisngid259157-
dc.contributor.daisngid1492715-
dc.contributor.daisngid1625336-
dc.contributor.daisngid6736194-
dc.contributor.daisngid499760-
dc.contributor.daisngid16365767-
dc.identifier.investigatorRIDK-4379-2013-
dc.identifier.investigatorRIDC-3915-2017-
dc.identifier.investigatorRIDB-7754-2008-
dc.identifier.investigatorRIDN-5758-2014-
dc.identifier.investigatorRIDG-6692-2016-
dc.identifier.investigatorRIDC-1082-2018-
dc.identifier.investigatorRIDN-5977-2014-
dc.identifier.investigatorRIDF-6503-2012-
dc.identifier.investigatorRIDN-5967-2014-
dc.identifier.investigatorRIDNo ID-
dc.identifier.externalWOS:000319445600072-
dc.contributor.wosstandardWOS:Lopez-de-Ipina, K
dc.contributor.wosstandardWOS:Alonso, JB
dc.contributor.wosstandardWOS:Travieso, CM
dc.contributor.wosstandardWOS:Sole-Casals, J
dc.contributor.wosstandardWOS:Egiraun, H
dc.contributor.wosstandardWOS:Faundez-Zanuy, M
dc.contributor.wosstandardWOS:Ezeiza, A
dc.contributor.wosstandardWOS:Barroso, N
dc.contributor.wosstandardWOS:Ecay-Torres, M
dc.contributor.wosstandardWOS:Martinez-Lage, P
dc.contributor.wosstandardWOS:de Lizardui, UM
dc.date.coverdateEnero 2013
dc.identifier.ulpgces
dc.description.sjr0,627
dc.description.jcr2,048
dc.description.sjrqQ1
dc.description.jcrqQ2
dc.description.scieSCIE
item.fulltextCon texto completo-
item.grantfulltextopen-
crisitem.author.deptGIR IDeTIC: División de Procesado Digital de Señales-
crisitem.author.deptIU para el Desarrollo Tecnológico y la Innovación-
crisitem.author.deptDepartamento de Señales y Comunicaciones-
crisitem.author.deptGIR IDeTIC: División de Procesado Digital de Señales-
crisitem.author.deptIU para el Desarrollo Tecnológico y la Innovación-
crisitem.author.deptDepartamento de Señales y Comunicaciones-
crisitem.author.orcid0000-0002-7866-585X-
crisitem.author.orcid0000-0002-4621-2768-
crisitem.author.parentorgIU para el Desarrollo Tecnológico y la Innovación-
crisitem.author.parentorgIU para el Desarrollo Tecnológico y la Innovación-
crisitem.author.fullNameAlonso Hernández, Jesús Bernardino-
crisitem.author.fullNameTravieso González, Carlos Manuel-
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