- 33 - Special Issue on 3D Medicine and Artificial Intelligence * Corresponding author. E-mail address: esmaeilpour@iauh.ac.ir I. IntRoductIon E PIlePsy is one of the most prevalent neurological disorders among people[1]. It is estimated that 5 people are afflicted with epilepsy among each 1000 people. Epilepsy could be defned as a sudden change in the intracellular and extracellular potential difference. This defnition implies that the type of neuron determines clinical demonstrations[2]. The automatic diagnosis of epileptic convulsions has attracted the attention of clinicians and engineers since 1970. The automatic prediction of seizures is useful in drug delivery systems and neural stimulation simulation devices [3, 4]. An important issue in predicting epileptic convulsions is that they are predictable through analyzing the changes in the features of EEG signals that happen before the occurrence of seizures [5]. Epileptic seizures prediction needs further analysis due to the following reasons [6]: 1. Generally, their results are not repeatable. In other words, their confidence rate is not certain. 2. The dependence of the result on sensitivity and inaccurate prediction rate is not taken into account. 3. Their efficiency is not mostly acceptable and has a high acceptance and rejection rate. II. MateRIals and Methods In an automatic epileptic convulsion detection system, a distinction should be made between the pre-convulsion, during convulsion, and post-convulsion EEG signals. Then, they should be analyzed [7]. Some studies focused on single-channel EEG signals, while some others focused on multi-channel recorded EEG signals [8]. This paper studied the epileptic and healthy signals of R. G. Andrzejak database from the University of Bonn [9]. The data relate to three different categories:normal situation of the patient, pre-seizure and seizure. The collected EEG signals include 5 categories which, respectively, are called (A,B,C,D,E).Each of these categories includes 100 single- channel signals with a length of 26.3 seconds. Category A: Surface EEG signal recorded from 5 healthy volunteers in a relaxed awake state with eyes open. Category B: EEG signal recorded from 5 healthy volunteers in a relaxed state with eyes closed. Category C: Deep signals recorded from epileptic patients during the interval between seizures from inside the area that caused the seizure. (focal signals) Category D: Deep recorded signals from epileptic patients during the intervals between seizures from outside the area that caused the seizure. (non-focal signals) Category E: Signals recorded from epileptic seizures. All EEG signals were recorded with the 128-channel system with DOI: 10.9781/ijimai.2017.456 Keywords discrete Wavelet Transforms (DWT), Accuracy, Electroencephalogram Signals (EEG), Multilayer Perceptron (MLP), Epileptic Seizure. Abstract Electroencephalogram signals (EEG) have always been used in medical diagnosis. Evaluation of the statistical characteristics of EEG signals is actually the foundation of all brain signal processing methods. Since the correct prediction of disease status is of utmost importance, the goal is to use those models that have minimum error and maximum reliability. In anautomatic epileptic seizure detection system, we should be able to distinguish between EEG signals before, during and after seizure. Extracting useful characteristics from EEG data can greatly increase the classification accuracy. In this new approach, we first parse EEG signals to sub-bands in different categories with the help of discrete wavelet transform(DWT) and then we derive statistical characteristics such as maximum, minimum, average and standard deviation for each sub-band. A multilayer perceptron (MLP)neural network was used to assess the different scenarios of healthy and seizure among the collected signal sets. In order to assess the success and effectiveness of the proposed method, the confusion matrix was used and its accuracy was achieved98.33 percent. Due to the limitations and obstacles in analyzing EEG signals, the proposed method can greatly help professionals experimentally and visually in the classification and diagnosis of epileptic seizures. Selecting Statistical Characteristics of Brain Signals to Detect Epileptic Seizures using Discrete Wavelet Transform and Perceptron Neural Network Rezvan Abbasi 1 , and Mansour Esmaeilpour 2 * 1 Department of Computer System Architecture, Arak Branch, Islamic Azad University, Arak (Iran) 2 Department of Computer Engineering, Hamedan Branch , Islamic Azad University, Hamedan (Iran) Received 26 June 2016 | Accepted 1 November 2016 | Published 23 December 2016