  Epilepsy is the most common neurological disorder and an accurate forecast of
seizures would help to overcome the patient's uncertainty and helplessness. In
this contribution, we present and discuss a novel methodology for the
classification of intracranial electroencephalography (iEEG) for seizure
prediction. Contrary to previous approaches, we categorically refrain from an
extraction of hand-crafted features and use a convolutional neural network
(CNN) topology instead for both the determination of suitable signal
characteristics and the binary classification of preictal and interictal
segments. Three different models have been evaluated on public datasets with
long-term recordings from four dogs and three patients. Overall, our findings
demonstrate the general applicability. In this work we discuss the strengths
and limitations of our methodology.
