Staging Epileptogenesis with Deep Neural Networks
This work addresses the need for early detection and monitoring of EPG in epilepsy patients, potentially enabling targeted interventions to slow disease progression, though it is incremental as it applies existing DNN methods to a new biomedical application.
The paper tackled the problem of staging epileptogenesis (EPG) by using deep neural networks to classify EEG signals from different phases in a rodent model, achieving average AUC scores of 0.93, 0.89, and 0.86 for distinguishing baseline, post-stimulation, and pre-seizure phases.
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.