  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
