Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/44018
Title: On the selection of non-invasive methods based on speech analysis oriented to automatic Alzheimer disease diagnosis
Authors: López-de-Ipiña, Karmele
Alonso, Jesus-Bernardino 
Manuel Travieso, Carlos 
Solé-Casals, Jordi
Egiraun, Harkaitz
Faundez-Zanuy, Marcos
Ezeiza, Aitzol
Barroso, Nora
Ecay-Torres, Miriam
Martinez-Lage, Pablo
De Lizardui, Unai Martinez
UNESCO Clasification: 3307 Tecnología electrónica
Keywords: Alzheimer’s disease diagnosis; spontaneous speech; emotion recognition; machine learning; non-invasive diagnostic techniques; dementia
Issue Date: 2013
Publisher: 1424-8220
Journal: Sensors 
Abstract: 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.
URI: http://hdl.handle.net/10553/44018
ISSN: 1424-8220
DOI: 10.3390/s130506730
Source: Sensors[ISSN 1424-8220],v. 13 (5), p. 6730-6745
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