Epilepsy is a chronic neurological disorder that occurs in the brain،
and affects approximately 2% of people around the world، where
epilepsy patients face a lot of difficulties in everyday life due to the
occurrence of seizures. Electroencephalog
ram (EEG) is used in
the automated detection of epileptic seizures، which has
Characteristics of non-linear and non-stationary. In this research،
we conducted automated detection of the seizures from the scalp
EEG signals using a Level 5 Discrete Wavelet Transforms DWT to
analyze the signal and extracting statistical features (maximum،
minimum، mean، average ، standard deviation، the ratio between
the mean values) and Categorizing using artificial neural networks
ANN for classification. The suggested detection method has
89.85% detection accuracy with 90.60% sensitivity ، and 89.1%
specificity.
