Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/43994
Title: On the alzheimer’s disease diagnosis: Automatic spontaneous speech analysis
Authors: Lopez-De-Ipiña, K.
Solé-Casals, J.
Alonso, J. B. 
Travieso, C. M. 
Ecay, M.
Martinez-Lage, P.
UNESCO Clasification: 3307 Tecnología electrónica
Keywords: Dementia
Alzheimer’s disease diagnosis
Spontaneous speech
Emotion recognition
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
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