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dc.contributorUniversitat de Vic. Escola Politècnica Superior
dc.contributorUniversitat de Vic. Grup de Recerca en Tecnologies Digitals
dc.contributor.authorGallego Jutglà, Esteve
dc.contributor.authorSolé-Casals, Jordi
dc.date.accessioned2014-03-20T11:05:02Z
dc.date.available2014-03-20T11:05:02Z
dc.date.created2013
dc.date.issued2013
dc.identifier.citationGallego-Jutglà, E., & Sole-Casals, J. (2013). In Gibert K., Botti V. and Reig-Bolano R.(Eds.), Improving early diagnosis of alzheimer's disease using synchrony measures A: Frontiers in Artificial Intelligence and Applications, vol. 256, IOS Press, 2013, p. 167-170 doi:10.3233/978-1-61499-320-9-167ca_ES
dc.identifier.isbn9781614993193
dc.identifier.issn0922-6389
dc.identifier.urihttp://hdl.handle.net/10854/2792
dc.description.abstractIt is well-known that Alzheimer's disease causes changes on the electroencephalography of the patients. However those changes are difficult to parameterize. In this paper a new ratio between synchrony in 0 and a band is investigated in arder to get an early diagnosis of Mild Alzheimer's patients. The presented ratio is compared using two types of classifiers, Linear Discriminan! Analysis and Artificial Neural Networks, with values of synchrony in the standard frequency bands. Presented results improve using the ratio in the linear classifier. Using the non-linear classifíer, best results are obtained using synchrony measures in 0 and a band simultaneously.en
dc.formatapplication/pdf
dc.format.extent4 p.ca_ES
dc.language.isoengca_ES
dc.publisherIOS Pressca_ES
dc.rights(c) 2013, IOS Press
dc.rightsTots els drets reservatsca_ES
dc.subject.otherAlzheimer, Malaltia d'ca_ES
dc.titleImproving Early Diagnosis of Alzheimer's Disease Using Synchrony Measuresen
dc.typeinfo:eu-repo/semantics/bookPartca_ES
dc.identifier.doihttps://doi.org/:I0.3233/978-1-61499-320-9-167
dc.relation.publisherversionhttp://ebooks.iospress.nl/publication/35243
dc.rights.accessRightsinfo:eu-repo/semantics/closedAccessca_ES


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