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Epilepsy is a neurological condition such that it affects the brain and thenervous system. It is characterized by recurrent seizures, which are physicalreactions to sudden, usually brief, excessive electrical discharges in a groupof brain cells. Hence, seizure identification has great importance in clinicaltherapy of epileptic patients. Electroencephalogram (EEG) is one of the mainbiomarker that can measure voltage fluctuations of the brain and EEG dataanalysis helps to investigate the patient with epilepsy syndrome as epilepsyleaves their signature in EEG signals. In this paper, the Discrete WaveletTransform (DWT) is applied to EEG signals to pre-processing, decompose it tillthe 4th level of decomposition tree.Various features like Entropy, Min, Max,Mean, Median, Standard deviation, Variance, Skewness, Energy and Relative WaveEnergy (RWE) were computed in terms of detailed coefficients and theapproximation coefficients of the last decomposition level.Then, the extractedfeatures are evaluated by three modern machine-learning classifiers such asRadial Basis Function based Support Vector Machine (SVMRBF), k-Nearest Neighbor(KNN) and Naive Bayes (NB). The experimental results demonstrate that thehighest classification accuracy (100\%) for normal subject data versusepileptic data is obtained by SVMRBF. the corresponding accuracy between normalsubject data and epileptic data using KNN and NB is obtained as 99.50\% and99\% for the eyes open and eyes closed conditions, respectively. The similaraccuracies, while comparing the interictal and ictal data, are obtained as99\%, 97.50\% and 98.50\% using the SVMRBF, KNN and NB classifiers,respectively. These accuracies are quite higher than earlier results published.

A hybrid automated detection of epileptic seizures in EEG based on wavelet and machine learning techniques