  Epilepsy is the second most common brain disorder after migraine. Automatic
detection of epileptic seizures can considerably improve the patients' quality
of life. Current Electroencephalogram (EEG)-based seizure detection systems
encounter many challenges in real-life situations. The EEGs are non-stationary
signals and seizure patterns vary across patients and recording sessions.
Moreover, EEG data are prone to numerous noise types that negatively affect the
detection accuracy of epileptic seizures. To address these challenges, we
introduce the use of a deep learning-based approach that automatically learns
the discriminative EEG features of epileptic seizures. Specifically, to reveal
the correlation between successive data samples, the time-series EEG data are
first segmented into a sequence of non-overlapping epochs. Second, Long
Short-Term Memory (LSTM) network is used to learn the high-level
representations of the normal and the seizure EEG patterns. Third, these
representations are fed into Softmax function for training and classification.
The results on a well-known benchmark clinical dataset demonstrate the
superiority of the proposed approach over the existing state-of-the-art
methods. Furthermore, our approach is shown to be robust in noisy and real-life
conditions. Compared to current methods that are quite sensitive to noise, the
proposed method maintains its high detection performance in the presence of
common EEG artifacts (muscle activities and eye-blinking) as well as white
noise.
