dc.contributor.author | López de Ipiña Peña, Miren Karmele | |
dc.contributor.author | Alonso, Jesús Bernardino | |
dc.contributor.author | Travieso, Carlos Manuel | |
dc.contributor.author | Sole-Casals, Jordi | |
dc.contributor.author | Eguiraun Martínez, Harkaitz | |
dc.contributor.author | Faúndez Zanuy, Marcos | |
dc.contributor.author | Ezeiza Ramos, Aitzol | |
dc.contributor.author | Barroso Moreno, Nora | |
dc.contributor.author | Ecay Torres, Miriam | |
dc.contributor.author | Martínez-Lage Alvarez, Pablo | |
dc.contributor.author | Martínez de Lizarduy Sturtze, Unai | |
dc.date.accessioned | 2014-02-04T16:52:31Z | |
dc.date.available | 2014-02-04T16:52:31Z | |
dc.date.issued | 2013-05 | |
dc.identifier.citation | Sensors 13(5) : 6730-6745 (2013) | es |
dc.identifier.issn | 1424-8220 | |
dc.identifier.uri | http://hdl.handle.net/10810/11337 | |
dc.description.abstract | The work presented here is part of a larger study to identify novel technologies and biomarkers for early Alzheimer disease (AD) detection and it focuses on evaluating the suitability of a new approach for early AD diagnosis by non-invasive methods. The purpose is to examine in a pilot study the potential of applying intelligent algorithms to speech features obtained from suspected patients in order to contribute to the improvement of diagnosis of AD and its degree of severity. In this sense, Artificial Neural Networks (ANN) have been used for the automatic classification of the two classes (AD and control subjects). Two human issues have been analyzed for feature selection: Spontaneous Speech and Emotional Response. Not only linear features but also non-linear ones, such as Fractal Dimension, have been explored. The approach is non invasive, low cost and without any side effects. Obtained experimental results were very satisfactory and promising for early diagnosis and classification of AD patients. | es |
dc.description.sponsorship | his work has been partially supported by a SAIOTEK from the Basque Government, University of Vic under the research grant R0904, and the Spanish Ministerio de Ciencia e Innovacion TEC2012-38630-C04-03. Iciar Martinez (Research Center for Experimental Marine Biology and Biotechnology-Plentziako Itsas Estazioa (PIE), University of the Basque Country & IKERBASQUE, Basque Foundation for Science | es |
dc.language.iso | eng | es |
dc.publisher | MDPI | es |
dc.relation | info:eu-repo/grantAgreement/MINECO/TEC2012-38630-C04-03 | |
dc.rights | info:eu-repo/semantics/openAccess | es |
dc.subject | Alzheimer's disease diagnosis | es |
dc.subject | spontaneous speech | es |
dc.subject | emotion recognition; | es |
dc.subject | machine learning | es |
dc.subject | non-invasive diagnostic techniques | es |
dc.subject | dementia | es |
dc.title | On the Selection of Non-Invasive Methods Based on Speech Analysis Oriented to Automatic Alzheimer Disease Diagnosis | es |
dc.type | info:eu-repo/semantics/article | es |
dc.rights.holder | © 2013 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license http://creativecommons.org/licenses/by/3.0/) | es |
dc.relation.publisherversion | http://www.mdpi.com/1424-8220/13/5/6730 | es |
dc.identifier.doi | 10.3390/s130506730 | |
dc.departamentoes | Ingeniería de sistemas y automática | es_ES |
dc.departamentoeu | Sistemen ingeniaritza eta automatika | es_ES |
dc.subject.categoria | BIOCHEMISTRY AND MOLECULAR BIOLOGY | |
dc.subject.categoria | PHYSICS, ATOMIC, MOLECULAR AND CHEMICAL | |
dc.subject.categoria | ELECTRICAL AND ELECTRONIC ENGINEERING | |
dc.subject.categoria | CHEMISTRY, ANALYTICAL | |