Home » Research&Publications » A DWT-Entropy-ANN Based Architecture for Epilepsy Diagnosis Using EEG Signals

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

Atsip2016Salah satu paper terbaru kami di IEEE conference “A DWT-Entropy-ANN Based Architecture for Epilepsy Diagnosis Using EEG Signals”. Link untuk download paper secara lengkap di ieeexplore disini.

Abstract: 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%)‎

 

 

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