EEG analysis – automatic spike detection
Algimantas Juozapavičius
Vilnius University, Lithuania
Gytis Bacevičius
Vilnius University, Lithuania
Dmitrijus Bugelskis
Vilnius University, Lithuania
Rūta Samaitienė
Vilnius University, Lithuania
Published 2012-01-25


rolandic epilepsy
epileptic spikes
morphological filters

How to Cite

Juozapavičius A., Bacevičius G., Bugelskis D. and Samaitienė R. (2012) “EEG analysis – automatic spike detection”, Nonlinear Analysis: Modelling and Control, 16(4), pp. 375-386. doi: 10.15388/NA.16.4.14083.


In the diagnosis and treatment of epilepsy, an electroencephalography (EEG) is one of the main tools. However visual inspection of EEG is very time consuming. Automatic extraction of important EEG features saves not only a lot of time for neurologist, but also enables a whole new level for EEG analysis, by using data mining methods. In this work we present and analyse methods to extract some of these features of EEG – drowsiness score and centrotemporal spikes. For spike detection, a method based on morphological filters is used. Also a database design is proposed in order to allow easy EEG analysis and provide data accessibility for data mining algorithms developed in the future.

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