  Epilepsy is a brain disorder due to abnormalactivity of neurons and recording
of seizures is of primary interest in the evaluation of epileptic patients. A
seizureis the phenomenon of rhythmicity discharge from either a local area or
the whole brain and the individual behavior usually lasts from seconds to
minutes.In this work, empirical wavelet transform(EWT) is applied to decompose
signals into Electroencephalography (EEG) rhythms. EEG signals are separated to
delta, theta, alpha, beta and gamma rhythms using EWT.The proposed method has
been evaluated by benchmark dataset which is freely downloadable from Bonn
University website. 95% confident ellipse area is computed from 2D projection
of reconstructed phase space (RPS)of rhythms as features and fed to K-nearest
neighbor classifier for detection of seizure (S) and seizure free (SF) EEG
signals. Our proposed method archived 98% accuracy in classification of S and
SF EEG signals with a tenfold cross-validation strategy that is higher than
previous techniques.
