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

TL;DR

This paper presents a non-linear, language-independent method for detecting Mild Cognitive Impairment using speech analysis of Categorical Verbal Fluency, employing SVM classification with fractal and entropy features.

Contribution

It introduces a novel approach combining non-linear features and feature selection for MCI detection from speech data.

Findings

Over 50% accuracy in MCI detection

Effective feature selection with ANOVA

Use of fractal dimension and permutation entropy

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