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 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.

Automatic analysis of Categorical Verbal Fluency for Mild Cognitive Impartment detection: a non-linear language independent approach

Alzheimer's disease (AD) is one the main causes of dementia in the world and the patients develop severe disability and sometime full dependence. In previous stages Mild Cognitive Impairment (MCI) produces cognitive loss but not severe enough to interfere with daily life. This work, on selection of biomarkers from speech for the detection of AD, is part of a wide-ranging cross study for the diagnosis of Alzheimer. Specifically in this work a task for detection of MCI has been used. The task analyzes Categorical Verbal Fluency. The automatic classification is carried out by SVM over classical linear features, Castiglioni fractal dimension and Permutation Entropy. Finally the most relevant features are selected by ANOVA test. The promising results are over 50% for MCI