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Epilepsy is a brain disorder due to abnormalactivity of neurons and recordingof seizures is of primary interest in the evaluation of epileptic patients. Aseizureis the phenomenon of rhythmicity discharge from either a local area orthe whole brain and the individual behavior usually lasts from seconds tominutes.In this work, empirical wavelet transform(EWT) is applied to decomposesignals into Electroencephalography (EEG) rhythms. EEG signals are separated todelta, theta, alpha, beta and gamma rhythms using EWT.The proposed method hasbeen evaluated by benchmark dataset which is freely downloadable from BonnUniversity website. 95% confident ellipse area is computed from 2D projectionof reconstructed phase space (RPS)of rhythms as features and fed to K-nearestneighbor classifier for detection of seizure (S) and seizure free (SF) EEGsignals. Our proposed method archived 98% accuracy in classification of S andSF EEG signals with a tenfold cross-validation strategy that is higher thanprevious techniques.

Classification of seizure and seizure-free EEG signals based on empirical wavelet transform and phase space reconstruction