Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/74205
Title: On automatic diagnosis of Alzheimer's Disease based on spontaneous speech analysis and emotional temperature
Authors: Lopez-de-Ipina, K.
Alonso, J. B. 
Sole-Casals, J.
Barroso, N.
Henríquez Sánchez, Patricia 
Faundez-Zanuy, M.
Travieso González, Carlos Manuel 
Ecay-Torres, M.
Martinez-Lage, P.
Eguiraun, H.
UNESCO Clasification: 32 Ciencias médicas
33 Ciencias tecnológicas
Keywords: Dementia
Alzheimer'S Disease Diagnosis
Spontaneous Speech
Emotion Recognition
Issue Date: 2015
Journal: Cognitive Computation 
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/10553/74205
ISSN: 1866-9956
DOI: 10.1007/s12559-013-9229-9
Source: Cognitive Computation [ISSN 1866-9956], v. 7 (1), p. 44-55, (Febrero 2015)
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