![]() For the Bern-Barcelona EEG, we obtained an increase in accuracy from 92.3% to 98.9% when classifying between focal and non-focal signals using the empirical mode decomposition. ![]() ![]() This analysis was carried out using two public datasets (Bern-Barcelona EEG and Epileptic Seizure Recognition datasets) obtaining significant improvements in accuracy. We evaluated several EEG signal transforms for generating the inputs to the deep neural network: Fourier, wavelet and empirical mode decomposition. The deep learning architecture is made up of two convolutional layers for feature extraction and three fully-connected layers for classification. This paper describes the analysis of a deep neural network for the classification of epileptic EEG signals. ![]()
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