Detecting complexity abnormalities in dyslexia measuring approximate entropy of electroencephalographic signals

Andreadis, II; Giannakakis, GA; Papageorgiou, C; Nikita, KS

dc.contributor.author	Andreadis, II	en
dc.contributor.author	Giannakakis, GA	en
dc.contributor.author	Papageorgiou, C	en
dc.contributor.author	Nikita, KS	en
dc.date.accessioned	2014-03-01T02:46:06Z
dc.date.available	2014-03-01T02:46:06Z
dc.date.issued	2009	en
dc.identifier.issn	1557170X	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/32545
dc.subject	Electroencephalogram	en
dc.subject	Reading Disability	en
dc.subject	Statistical Analysis	en
dc.subject	Time Series Data	en
dc.subject	Control Subjects	en
dc.subject.other	Approximate entropy	en
dc.subject.other	Asynchrony	en
dc.subject.other	Classification scheme	en
dc.subject.other	EEG signals	en
dc.subject.other	Electroencephalogram signals	en
dc.subject.other	Electroencephalographic signals	en
dc.subject.other	Feature input	en
dc.subject.other	Physiological signals	en
dc.subject.other	Statistical analysis	en
dc.subject.other	Statistical parameters	en
dc.subject.other	Time-series data	en
dc.subject.other	Biology	en
dc.subject.other	Physiological models	en
dc.subject.other	Time series	en
dc.subject.other	Electroencephalography	en
dc.title	Detecting complexity abnormalities in dyslexia measuring approximate entropy of electroencephalographic signals	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/IEMBS.2009.5332798	en
heal.identifier.secondary	http://dx.doi.org/10.1109/IEMBS.2009.5332798	en
heal.identifier.secondary	5332798	en
heal.publicationDate	2009	en
heal.abstract	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. ©2009 IEEE.	en
heal.journalName	Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009	en
dc.identifier.doi	10.1109/IEMBS.2009.5332798	en
dc.identifier.volume	2009	en
dc.identifier.spage	6292	en
dc.identifier.epage	6295	en