  Epilepsy is one of the most common neurological disorders, affecting about 1%
of the population at all ages. Detecting the development of epilepsy, i.e.,
epileptogenesis (EPG), before any seizures occur could allow for early
interventions and potentially more effective treatments. Here, we investigate
if modern machine learning (ML) techniques can detect EPG from intra-cranial
electroencephalography (EEG) recordings prior to the occurrence of any
seizures. For this we use a rodent model of epilepsy where EPG is triggered by
electrical stimulation of the brain. We propose a ML framework for EPG
identification, which combines a deep convolutional neural network (CNN) with a
prediction aggregation method to obtain the final classification decision.
Specifically, the neural network is trained to distinguish five second segments
of EEG recordings taken from either the pre-stimulation period or the
post-stimulation period. Due to the gradual development of epilepsy, there is
enormous overlap of the EEG patterns before and after the stimulation. Hence, a
prediction aggregation process is introduced, which pools predictions over a
longer period. By aggregating predictions over one hour, our approach achieves
an area under the curve (AUC) of 0.99 on the EPG detection task. This
demonstrates the feasibility of EPG prediction from EEG recordings.
