Detecting complexity abnormalities in dyslexia measuring approximate entropy of electroencephalographic signals

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.