DSpace Repository

Feature selection for spontaneous speech analysis to aid in Alzheimer’s disease diagnosis: A fractal dimension approach

Show simple item record

dc.contributor Universitat de Vic. Escola Politècnica Superior
dc.contributor.author Lopez-de-Ipiña, Karmele
dc.contributor.author Solé-Casals, Jordi
dc.contributor.author Eguiraun, Harkaitz
dc.contributor.author Alonso, Jesús B.
dc.contributor.author Travieso, Carlos M.
dc.contributor.author Ezeiza, Aitzol
dc.contributor.author Barroso, Nora
dc.contributor.author Ecay-Torres, Miriam
dc.contributor.author Martinez-Lage, Pablo
dc.contributor.author Beitia, Blanca
dc.date.accessioned 2015-01-13T19:10:58Z
dc.date.available 2015-01-13T19:10:58Z
dc.date.created 2015
dc.date.issued 2015
dc.identifier.citation López-De-Ipiña, K., Solé-Casals, J., Eguiraun, H., Alonso, J. B., Travieso, C. M., Ezeiza, A., et al. (2015). Feature selection for spontaneous speech analysis to aid in alzheimer's disease diagnosis: A fractal dimension approach. Computer Speech and Language, 30(1), 43-60. ca_ES
dc.identifier.issn 0885-2308
dc.identifier.uri http://hdl.handle.net/10854/3804
dc.description.abstract Alzheimer’s disease (AD) is the most prevalent form of degenerative dementia; it has a high socio-economic impact in Westerncountries. The purpose of our project is to contribute to earlier diagnosis of AD and allow better estimates of its severity by usingautomatic analysis performed through new biomarkers extracted through non-invasive intelligent methods. The method selectedis based on speech biomarkers derived from the analysis of spontaneous speech (SS). Thus the main goal of the present work isfeature search in SS, aiming at pre-clinical evaluation whose results can be used to select appropriate tests for AD diagnosis. Thefeature set employed in our earlier work offered some hopeful conclusions but failed to capture the nonlinear dynamics of speechthat are present in the speech waveforms. The extra information provided by the nonlinear features could be especially useful whentraining data is limited. In this work, the fractal dimension (FD) of the observed time series is combined with linear parameters inthe feature vector in order to enhance the performance of the original system while controlling the computational cost.© 2014 Elsevier Ltd. All rights reserved. en
dc.format application/pdf
dc.format.extent 18 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/doi:10.1016/j.csl.2014.08.002 ca_ES
dc.subject.other Alzheimer, Malaltia d' ca_ES
dc.subject.other Processament de la parla ca_ES
dc.title Feature selection for spontaneous speech analysis to aid in Alzheimer’s disease diagnosis: A fractal dimension approach en
dc.type info:eu-repo/semantics/article ca_ES
dc.identifier.doi https://doi.org/doi:10.1016/j.csl.2014.08.002
dc.rights.accesRights info:eu-repo/semantics/closedAccess ca_ES
dc.type.version info:eu-repo/publishedVersion ca_ES
dc.indexacio Indexat a SCOPUS
dc.indexacio Indexat a WOS/JCR ca_ES

Files in this item

Show simple item record

Search RIUVic