Wavelet based classification of epileptic seizures in EEG signals

Epilepsy is a chronic neurological disorder characterized by recurrent, sudden discharges of cerebral neurons, called seizures. Seizures are not always clearly defined and have extremely varied morphologies. Neurophysiologists are not always able to discriminate seizures, especially in long-term EEG datasets. Affecting 1% of the world's population with 1/3 of the epileptic patients not corresponding to anti-epileptic medication, epilepsy is constantly under the microscope and systems for automated detection of seizures are thoroughly examined. In this paper, a method for automated detection of epileptic activity is presented. The Discrete Wavelet Transform (DWT) is used to decompose the EEG recordings in several subbands and five features are extracted from the wavelet coefficients creating a set of features. The extracted feature vector is used to train a Support Vector Machine (SVM) classifier. Five classification problems are addressed, reaching high levels of overall accuracy ranging from 87% to 100%.