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. Electroencephalogram (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.