  Epilepsy is a common neurological disorder characterized by recurrent
seizures accompanied by excessive synchronous brain activity. The process of
structural and functional brain alterations leading to increased seizure
susceptibility and eventually spontaneous seizures is called epileptogenesis
(EPG) and can span months or even years. Detecting and monitoring the
progression of EPG could allow for targeted early interventions that could slow
down disease progression or even halt its development. Here, we propose an
approach for staging EPG using deep neural networks and identify potential
electroencephalography (EEG) biomarkers to distinguish different phases of EPG.
Specifically, continuous intracranial EEG recordings were collected from a
rodent model where epilepsy is induced by electrical perforant pathway
stimulation (PPS). A deep neural network (DNN) is trained to distinguish EEG
signals from before stimulation (baseline), shortly after the PPS and long
after the PPS but before the first spontaneous seizure (FSS). Experimental
results show that our proposed method can classify EEG signals from the three
phases with an average area under the curve (AUC) of 0.93, 0.89, and 0.86. To
the best of our knowledge, this represents the first successful attempt to
stage EPG prior to the FSS using DNNs.
