A DWT-Entropy-ANN Based Architecture for Epilepsy Diagnosis Using EEG Signals

Conference Paper

Alsuwailem, Khalil AlSharabi, Sutrisno Ibrahim, Ridha Djemal, Abdullah . 2016

• نوع عمل المنشور: PhD
• الوسوم: EEG, Epilepsy, Wavelet, Entropy, ANN
• اسم المؤتمر: IEEE 2nd International Conference on Advanced Technologies for Signal and Image Processing
• عنوان المؤتمر: Tunis
• تاريخ المؤتمر: الخميس, نيسان (أبريل) 21, 2016

مستخلص المنشور

Electroencephalogram (EEG) is one the most common tools for epilepsy diagnosis and analysis‎. Currently, epilepsy diagnosis is still mainly performed by a neurologist through manual or visual inspection of EEG signals‎. In this article, we develop a computer aided diagnosis (CAD) for epilepsy based on discrete wavelet transform (DWT), Shannon entropy ‎and feed-forward neural network (FFNN). ‎DWT decompose ‎EEG signals into several frequency sub-bands such as delta, theta, ‎alpha, beta and gamma. Shannon entropy extract the EEG features from each these frequency sub-bands. ‏‎ Finally, FFNN classifies the corresponding ‎EEG signals ‎ into ‎‎“normal” or “epileptic” class based on the extracted features‎. Our experimental results using publicly available University of Bonn EEG dataset show ‎perfect accuracy (100%)‎