Selecting Statistical Characteristics of Brain Signals to Detect Epileptic Seizures using Discrete Wavelet Transform and Perceptron Neural Network

TitleSelecting Statistical Characteristics of Brain Signals to Detect Epileptic Seizures using Discrete Wavelet Transform and Perceptron Neural Network
Publication TypeJournal Article
Year of Publication2017
AuthorsAbbasi, R., and M. Esmaeilpour
JournalInternational Journal of Interactive Multimedia and Artificial Intelligence
ISSN1989-1660
IssueSpecial Issue on 3D Medicine and Artificial Intelligence
Volume4
Number5
Date Published09/2017
Pagination33-38
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.

KeywordsAccuracy, Discrete Wavelet Transforms, Medicine, Multilayer Perceptron, Neural Network
DOI10.9781/ijimai.2017.456
URLhttp://www.ijimai.org/journal/sites/default/files/files/2016/12/ijimai20174_5_6_pdf_12024.pdf
AttachmentSize
ijimai20174_5_6.pdf1.77 MB