Analysis of Disfluencies for automatic detection of Mild Cognitive Impartment: a deep learning approach

The so-called Mild Cognitive Impairment (MCI) or cognitive loss appears in a previous stage before Alzheimer's Disease (AD), but it does not seem sufficiently severe to interfere in independent abilities of daily life, so it usually does not receive an appropriate diagnosis. Its detection is a...

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Bibliographic Details
Published in:arXiv.org 2022-03
Main Authors: Lopez-de-Ipiña, Karmele, Unai Martinez de Lizarduy, Calvo, Pilar, Beita, Blanca, García-Melero, Joseba, Ecay-Torres, Miriam, Estanga, Ainara, Faundez-Zanuy, Marcos
Format: Article
Language:English
Subjects:
Alzheimer's disease
Artificial neural networks
Deep learning
Diagnosis
Support vector machines
Online Access:Get full text
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Summary:The so-called Mild Cognitive Impairment (MCI) or cognitive loss appears in a previous stage before Alzheimer's Disease (AD), but it does not seem sufficiently severe to interfere in independent abilities of daily life, so it usually does not receive an appropriate diagnosis. Its detection is a challenging issue to be addressed by medical specialists. This work presents a novel proposal based on automatic analysis of speech and disfluencies aimed at supporting MCI diagnosis. The approach includes deep learning by means of Convolutional Neural Networks (CNN) and non-linear multifeature modelling. Moreover, to select the most relevant features non-parametric Mann-Whitney U-testt and Support Vector Machine Attribute (SVM) evaluation are used.
ISSN:2331-8422
DOI:10.48550/arxiv.2203.11606