The electroencephalogram (EEG) is one of the most precious technologies to
understand the happenings inside our brain and further understand our body's
happenings. Automatic prediction of oncoming seizures using the EEG signals
helps the doctors and clinical experts and reduces their workload. This paper
proposes an end-to-end deep learning algorithm to fully automate seizure
prediction's laborious task without any heavy pre-processing on the EEG data or
feature engineering. The proposed deep learning network is a blend of signal
processing and deep learning pipeline, which automates the seizure prediction
framework using the EEG signals. This proposed model was evaluated on an open
EEG dataset, CHB-MIT. The network achieved an average sensitivity of
97.746\text{\%} and a false positive rate (FPR) of 0.2373 per hour