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Spontaneous Speech and Emotional Response modeling based on One-class classifier oriented to Alzheimer Disease diagnosis

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dc.contributor Universitat de Vic. Escola Politècnica Superior
dc.contributor Universitat de Vic. Grup de Recerca en Tecnologies Digitals
dc.contributor Mediterranean Conference on Medical and Biological Engineering and Computing (13è : 2013: Sevilla)
dc.contributor.author Lopez-de-Ipiña, Karmele
dc.contributor.author Alonso, Jesús B.
dc.contributor.author Barroso, Nora
dc.contributor.author Solé-Casals, Jordi
dc.contributor.author Ecay-Torres, Miriam
dc.contributor.author Martinez-Lage, Pablo
dc.contributor.author Zelarain, F.
dc.contributor.author Egiraun, Harkaitz
dc.contributor.author Travieso, Carlos M.
dc.date.accessioned 2014-04-30T09:34:29Z
dc.date.available 2015-04-30T23:05:00Z
dc.date.created 2014
dc.date.issued 2014
dc.identifier.citation K. Lopez-de-Ipiña, J. B. Alonso, N. Barroso, J. Solé-Casals, M. Ecay-Torres, P. Martinez-Lage, F. Zelarain, H. Egiraun, C. M. Travieso, “Spontaneous Speech and Emotional Response Modeling Based on One-Class Classifier Oriented to Alzheimer Disease Diagnosis”, XIII Mediterranean Conference on Medical and Biological Engineering and Computing 2013 IFMBE Proceedings Volume 41, 2014, pp 567-570 ca_ES
dc.identifier.isbn 978-3-319-00846-2
dc.identifier.issn 1680-0737
dc.identifier.uri http://hdl.handle.net/10854/3014
dc.description.abstract The purpose of our project is to contribute to earlier diagnosis of AD and better estimates of its severity by using automatic analysis performed through new biomarkers extracted from non-invasive intelligent methods. The methods selected in this case are speech biomarkers oriented to Sponta-neous Speech and Emotional Response Analysis. Thus the main goal of the present work is feature search in Spontaneous Speech oriented to pre-clinical evaluation for the definition of test for AD diagnosis by One-class classifier. One-class classifi-cation problem differs from multi-class classifier in one essen-tial aspect. In one-class classification it is assumed that only information of one of the classes, the target class, is available. In this work we explore the problem of imbalanced datasets that is particularly crucial in applications where the goal is to maximize recognition of the minority class as in medical diag-nosis. The use of information about outlier and Fractal Dimen-sion features improves the system performance. ca_ES
dc.format application/pdf
dc.format.extent 5 p. ca_ES
dc.language.iso eng ca_ES
dc.publisher Springer ca_ES
dc.rights (c) Springer (The original publication is available at www.springerlink.com)
dc.rights Tots els drets reservats ca_ES
dc.subject.other Alzheimer, Malaltia d' ca_ES
dc.subject.other Processament de la parla ca_ES
dc.title Spontaneous Speech and Emotional Response modeling based on One-class classifier oriented to Alzheimer Disease diagnosis ca_ES
dc.type info:eu-repo/semantics/bookPart ca_ES
dc.embargo.terms 12 mesos ca_ES
dc.identifier.doi https://doi.org/10.1007/978-3-319-00846-2_141
dc.relation.publisherversion http://link.springer.com/chapter/10.1007%2F978-3-319-00846-2_141
dc.rights.accesRights info:eu-repo/semantics/openAccess ca_ES

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