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dc.contributor.authorLópez-de-Ipiña, Karmele
dc.contributor.authorMartinez de lizarduy, Unai
dc.contributor.authorCalvo, Paula M.
dc.contributor.authorBeitia, Blanca
dc.contributor.authorGarcía Melero, J.
dc.contributor.authorFernández, E.
dc.contributor.authorEcay-Torres, Mirian
dc.contributor.authorFaundez-Zanuy, Marcos
dc.contributor.authorSanz, P.
dc.date.accessioned2023-11-17T12:41:18Z
dc.date.available2023-11-17T12:41:18Z
dc.date.issued2018-05-08
dc.identifier.citationLópez-de-Ipiña K, Martinez de Lizarduy U, Calvo PM, Beitia B, García Melero J, Fernández E, Ecay-Torres M, FAundez-Zanuy M, Sanz P. On the analysis of speech and disfluencies for automatic detection of Mild Cognitive Impairment. Neural Comput Applic. 2020;( 32):15761–15769. DOI: 10.1007/s00521-018-3494-1ca
dc.identifier.issn1433-3058ca
dc.identifier.urihttp://hdl.handle.net/20.500.12367/2498
dc.description.abstractAlzheimer’s disease is characterized by a progressive and irreversible cognitive deterioration. In a previous stage, the so-called Mild Cognitive Impairment or cognitive loss appears. Nevertheless, this previous stage does not seem sufficiently severe to interfere in independent abilities of daily life, so it is usually diagnosed inappropriately. Thus, its detection is a crucial challenge to be addressed by medical specialists. This paper presents a novel proposal for such early diagnosis based on automatic analysis of speech and disfluencies, and Deep Learning methodologies. The proposed tools could be useful for supporting Mild Cognitive Impairment diagnosis. The Deep Learning approach includes Convolutional Neural Networks and nonlinear multifeature modeling. [...]ca
dc.format.extent9 p.ca
dc.language.isoengca
dc.publisherSpringer Natureca
dc.relation.ispartofNeural Computing and Applications. 2020;32:15761–15769ca
dc.rights(c) López-de-Ipiña K, et al. 2018ca
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subject.otherMild cognitive impairmentca
dc.subject.otherAutomatic speech analysisca
dc.subject.otherDeep learningca
dc.subject.otherConvolutional neural networksca
dc.subject.otherNonlinear featuresca
dc.subject.otherDisfluenciesca
dc.titleOn the analysis of speech and disfluencies for automatic detection of Mild Cognitive Impairmentca
dc.typeinfo:eu-repo/semantics/articleca
dc.description.versioninfo:eu-repo/semantics/publishedVersionca
dc.rights.accessLevelinfo:eu-repo/semantics/openAccess
dc.embargo.termscapca
dc.identifier.doi10.1007/s00521-018-3494-1ca


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(c) López-de-Ipiña K, et al. 2018
Except where otherwise noted, this item's license is described as http://creativecommons.org/licenses/by/4.0/
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