Epilepsy is a neurological disease that affects nearly 60 million people around the world. Electroencephalography (EEG) and Machine Learning (ML) can be used to detect epileptic seizures, where ML algorithms depend on hand crafted features. Recently, deep learning (DL) methods became popular due to automatic feature learning. However, current DL based approaches mainly focuses on different DL architecture design, especially deeper DL networks. Only few works experiment modified EEG data as an input to DL models. In this work, we propose modified, pre-processed and combined EEG signals as an input to different DL models for epilepsy seizure classification rather than using raw EEG signals. A variety of pre-processed and combined EEG signals have been evaluated with three different DL architectures: A simple Deep Neural Network (DNN) model and a moderately complex 1D-Convolutional Neural Network (CNN) model and a complex 1D-CNN model. We perform several experiments for two-class and three-class epileptic seizure classification on UCI-Bonn dataset using the proposed EEG signals: (1)Original EEG signal of UCI-Bonn dataset, (2)standardized EEG signal, (3)original EEG signal combined with squared EEG signal, and then standardized, (4)original EEG signal combined with differentiated EEG signal, and then standardized, and (5)original EEG signal combined with magnitude of Fast Fourier Transform (FFT) of EEG signal, and then standardized (original + FFT). Extensive evaluations and comparisons show that the best results are achieved with original + FFT combination on all DL architectures. Using original + FFT EEG signal, competitive results can be achieved with simple and moderately complex DL models, by utilizing less computational resources.