  Epilepsy is the fourth most common neurological disorder, affecting about 1%
of the population at all ages. As many as 60% of people with epilepsy
experience focal seizures which originate in a certain brain area and are
limited to part of one cerebral hemisphere. In focal epilepsy patients, a
precise surgical removal of the seizure onset zone can lead to effective
seizure control or even a seizure-free outcome. Thus, correct identification of
the seizure onset zone is essential. For clinical evaluation purposes,
electroencephalography (EEG) recordings are commonly used. However, their
interpretation is usually done manually by physicians and is time-consuming and
error-prone. In this work, we propose an automated epileptic signal
classification method based on modern deep learning methods. In contrast to
previous approaches, the network is trained directly on the EEG recordings,
avoiding hand-crafted feature extraction and selection procedures. This
exploits the ability of deep neural networks to detect and extract relevant
features automatically, that may be too complex or subtle to be noticed by
humans. The proposed network structure is based on a convolutional neural
network with residual connections. We demonstrate that our network produces
state-of-the-art performance on two benchmark data sets, a data set from Bonn
University and the Bern-Barcelona data set. We conclude that modern deep
learning approaches can reach state-of-the-art performance on epileptic EEG
classification and automated seizure onset zone identification tasks when
trained on raw EEG data. This suggests that such approaches have potential for
improving clinical practice.
