Dyslexia constitutes a specific reading disability, a condition
characterized by severe difficulty in the mastery of reading despite
normal intelligence or adequate education. Electroencephalogram (EEG)
signal may be able to play an important role in the diagnosis of
dyslexia. The Approximate Entropy (ApEn) is a recently formulated
statistical parameter used to quantify the regularity of a time series
data of physiological signals. In this paper, we initially estimated the
ApEn values in signals recorded from controls subjects and dyslectic
children. These values were firstly used for the statistical analysis of
the two groups and secondly as feature input in a classification scheme.
We also used the cross-ApEn methodology to get a measure of the
asynchrony of the signals recorded from different electrodes. This
preliminary study provides promising results towards correct
identification of dyslexic cases, analyzing the corresponding EEG
signals
