EEG analysis – automatic spike detection - Juozapavičius, Algimantas; Bacevičius, Gytis; Bugelskis, Dmitrijus; Samaitienė, Rūta (Nonlinear Analysis: Modelling and Control, 2012)
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.