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Título:
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On the Selection of Non-Invasive Methods Based on Speech Analysis Oriented to Automatic Alzheimer Disease Diagnosis
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Autor/a:
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Lopez-de-Ipiña, Karmele; Alonso, Jesús B.; Travieso, Carlos M.; Solé-Casals, Jordi; Egiraun, Harkaitz; Faundez-Zanuy, Marcos; Ezeiza, Aitzol; Barroso, Nora; Ecay-Torres, Miriam; Martinez-Lage, Pablo; Martinez de Lizardui, Unai
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Otros autores:
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Universitat de Vic. Escola Politècnica Superior; Universitat de Vic. Grup de Recerca en Tecnologies Digitals |
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Notas:
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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. |
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Materia(s):
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-Alzheimer, Malaltia d'
-Processament de la parla |
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Derechos:
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Aquest document està subjecte a aquesta llicència Creative Commons
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
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Tipo de documento:
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Artículo
info:eu-repo/publishedVersion |
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Editor:
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MDPI
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Compartir:
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