This paper describes the analysis of a deep neural network for the classification of epileptic EEG signals. The deep learning architecture is made up of two convolutional layers for feature extraction and three fully-connected layers for classification. We evaluated several EEG signal transforms for generating the inputs to the deep neural network: Fourier, wavelet and 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. 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. For the Epileptic Seizure Recognition dataset, we evaluated several scenarios for seizure detection obtaining the best results when using the Fourier transform. The accuracy increased from 99.0% to 99.5% for classifying non-seizure vs. seizure recordings, from 91.7% to 96.5% when differentiating between healthy, non-focal and seizure recordings, and from 89.0% to 95.7% when considering healthy, focal and seizure recordings. •This paper analyzes the use of a deep neural network for epileptic EEG signal classification.•The deep architecture is composed of 2 convolutional layers for features extraction and 3 fully connected layers for classification.•Analysis of different inputs to the DNN: raw data, Fourier transform, wavelet coefficients and empirical mode decomposition.•EMD is interesting for classifying focal vs. non-focal signals and Fourier transform performs better for seizure detection.