Recently, many studies have been conducted on automated epileptic seizures detection. However, few of these techniques are applied in clinical settings for several reasons. One of them is the imbalanced nature of the seizure detection task. Additionally, the current detection techniques do not really generalize to other patient populations. To address these issues, we present in this paper a hybrid CNN-LSTM model robust to cross-site variability. We investigate the use of data augmentation (DA) methods as an efficient tool to solve imbalanced training problems. The model trained on the publicly Children's Hospital of Boston (CHB) data set achieved great performance on a french data set acquired at the Centre Hospitalier Universitaire of Angers (CHU). Results showed that this approach outperforms both other deep learning (DL) and state-of-the-art methods.