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

Abstract: 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