Brain is the most complex organ amongst all the systems in the human body. The study of the electrical signals produced by neural activities of the human brain is called Electroencephalogram. Electroencephalogram (EEG) is a technique, which is used to identify the neurological disorder of the brain. Epilepsy is one of the most common neurological disorders of brain. Epilepsy needs to be detected efficiently using required EEG feature extraction, such as mean, standard deviation, median, entropy, kurtosis, skewness, etc. The framework of proposed technique is an efficient EEG signal classification approach. The proposed approach is used to classify the EEG signal into two classes: epileptic seizure or not. Extraction of the options is done by applying separate ripple remodel (DWT) so as to decompose the graph signals into sub-bands. These options, extracted from details and approximation coefficients of DWT sub-bands, are used as input to Principal Component Analysis (PCA) The classification is based on reducing the feature dimension using PCA and deriving the Support Vector Machine (SVM), Neural Network Analysis (NNA), and K-Nearest Neighbour (K-NN). In classification of normal and epileptic, results obtained exhibited an accuracy of 100% by applying NNA and k-NN. It has been found that the computation time of NNA classifier is lesser than SVM and k-NN to provide 100% accuracy. So, the detection of an epileptic seizure based on DWT statistical features using NNA classifiers is more suitable in real time for a reliable, automatic epileptic seizure detection system to enhance the patient's care and the quality of life.