A DWT-entropy-ANN based architecture for epilepsy diagnosis using EEG signals

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%).