02058nas a2200229   4500000000100000000000100001008004100002260001200043653001300055653001900068653001300087653003200100653002600132100002400158700001800182245015100200856009800351300001000449490000600459520134900465022001401814       2017                            d  c09/201710aAccuracy10aNeural Network10aMedicine10aDiscrete Wavelet Transforms10aMultilayer Perceptron1 aMansour Esmaeilpour1 aRezvan Abbasi00aSelecting Statistical Characteristics of Brain Signals to Detect Epileptic Seizures using Discrete Wavelet Transform and Perceptron Neural Network  uhttp://www.ijimai.org/journal/sites/default/files/files/2016/12/ijimai20174_5_6_pdf_12024.pdf  a33-380 v43 aElectroencephalogram 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.  a1989-1660