|Title:||On the alzheimer’s disease diagnosis: Automatic spontaneous speech analysis||Authors:||Lopez-De-Ipiña, K.
Alonso, J. B.
Travieso, C. M.
|UNESCO Clasification:||3307 Tecnología electrónica||Keywords:||Dementia
Alzheimer’s disease diagnosis
|Issue Date:||2014||Publisher:||Springer||Journal:||Lecture Notes in Computer Science||Conference:||5th International Conference on Agents and Artificial Intelligence (ICAART 2013)||Abstract:||Alzheimer’s disease (AD) is the most prevalent form of progressive degenerative dementia; it has a high socioeconomic impact in Western countries. Therefore, it is one of the most active research areas today. Alzheimer’s disease is sometimes diagnosed by excluding other dementias, and definitive confirmation is only obtained through a postmortem study of the brain tissue of the patient. The work presented here is part of a larger study that aims to identify novel technologies and biomarkers for early AD detection, and it focuses on evaluating the suitability of a new approach for early diagnosis of AD by noninvasive methods. The purpose is to examine, in a pilot study, the potential of applying machine learning algorithms to speech features obtained from suspected Alzheimer’s disease sufferers in order to help diagnose this disease and determine its degree of severity. Two human capabilities relevant in communication have been analyzed for feature selection: spontaneous speech and emotional response. The experimental results obtained were very satisfactory and promising for the early diagnosis and classification of AD patients||URI:||http://hdl.handle.net/10553/43994||ISBN:||978-3-662-44993-6||ISSN:||0302-9743||DOI:||10.1007/978-3-662-44994-3_14||Source:||Transactions on Computational Collective Intelligence XVII. Lecture Notes in Computer Science, v. 8790 LNCS, p. 272-281|
|Appears in Collections:||Capítulo de libro|
Items in accedaCRIS are protected by copyright, with all rights reserved, unless otherwise indicated.