Journal of Applied Sciences1812-56541812-5662Asian Network for Scientific Information10.3923/jas.2013.207.219RavishD.K.DeviS. ShenbagaKrishnamoorthyS.G.KarthikeyanM.R.22013132Electroencephalographs are records of brain electrical activity.
It is an indispensable tool for diagnosing epileptic seizure. Manually reviewing
EEG recordings for detection of epilepsy pattern is a time-consuming process.
It is, therefore, necessary to automate the epileptic seizure detection. Further
analyzing after the detection using large data set is a good supplement to the
wide range of algorithms currently used for analysis. Seizure evolution is typically
a dynamic and non stationary process and the EEG signals are composed of many
frequency bands. The objective of this work was to determine features that differentiated
epileptic seizure from a normal activity. Subjects suffering from a commonly
occurring generalized epileptic seizure EEG segments and non seizure EEG segments
are used for the study. The Spectral and Statistical methods are applied to
the signal and the features are extracted. Spectral power, Standard deviation,
Variance, Root Mean Square and Measure of Spread are the features that differentiate
abnormal activity from normal activity whereas Median, Mode, Skewness, kurtosis
are not able to differentiate. This study gives a method to detection of seizure
and analysis in offline. It can be further extended to the real time. The algorithm
is tested with two different databases covering children and as well as adult
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