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Feature selection for automatic analysis of emotional response based on nonlinear speech modeling suitable for diagnosis of Alzheimer׳s disease

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dc.contributor Universitat de Vic. Escola Politècnica Superior
dc.contributor.author Lopez-de-Ipiña, Karmele
dc.contributor.author Alonso, Jesús B.
dc.contributor.author Travieso, Carlos M.
dc.contributor.author Solé-Casals, Jordi
dc.contributor.author Ezeiza, Aitzol
dc.contributor.author Faundez-Zanuy, Marcos
dc.contributor.author Beitia, Blanca
dc.contributor.author Calvo, Pilar
dc.date.accessioned 2015-01-30T08:27:30Z
dc.date.available 2015-01-30T08:27:30Z
dc.date.issued 2015
dc.identifier.citation Karmele Lopez de Ipina, Jesus B. Alonso, Carlos M. Travieso, Jordi Solé-Casals, Aitzol Ezeiza, Marcos Faundez-Zanuy, Blanca Beitia, Pilar Calvo, “Feature selection for automatic analysis of emotional response based on nonlinear speech modeling suitable for diagnosis of Alzheimer's disease”, Neurocomputing, Volume 150, Part B, 20 February 2015, Pages 392–401.
dc.identifier.issn 0925-2312
dc.identifier.uri http://hdl.handle.net/10854/3854
dc.description.abstract Alzheimer׳s disease (AD) is the most common type of dementia among the elderly. This work is part of a larger study that aims to identify novel technologies and biomarkers or features for the early detection of AD and its degree of severity. The diagnosis is made by analyzing several biomarkers and conducting a variety of tests (although only a post-mortem examination of the patients’ brain tissue is considered to provide definitive confirmation). Non-invasive intelligent diagnosis techniques would be a very valuable diagnostic aid. This paper concerns the Automatic Analysis of Emotional Response (AAER) in spontaneous speech based on classical and new emotional speech features: Emotional Temperature (ET) and fractal dimension (FD). This is a pre-clinical study aiming to validate tests and biomarkers for future diagnostic use. The method has the great advantage of being non-invasive, low cost, and without any side effects. The AAER shows very promising results for the definition of features useful in the early diagnosis of AD. ca_ES
dc.format application/pdf
dc.format.extent 24 p. ca_ES
dc.language.iso eng ca_ES
dc.publisher Elsevier ca_ES
dc.rights (c) 2015 Elsevier. Published article is available at: http://dx.doi.org/10.1016/j.neucom.2014.05.083
dc.rights Tots els drets reservats ca_ES
dc.subject.other Alzheimer, Malaltia d' ca_ES
dc.title Feature selection for automatic analysis of emotional response based on nonlinear speech modeling suitable for diagnosis of Alzheimer׳s disease ca_ES
dc.type info:eu-repo/semantics/article ca_ES
dc.identifier.doi https://doi.org/10.1016/j.neucom.2014.05.083
dc.rights.accesRights info:eu-repo/semantics/openAccess ca_ES
dc.type.version info:eu-repo/acceptedVersion ca_ES
dc.indexacio Indexat a WOS/JCR ca_ES

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