Towards Early Diagnosis of Epilepsy from EEG Data

TL;DR

This study demonstrates that machine learning, specifically a CNN with prediction aggregation, can effectively predict epileptogenesis from EEG data in a rodent model, enabling early diagnosis before seizures occur.

Contribution

The paper introduces a novel ML framework combining CNN and prediction aggregation for early epilepsy diagnosis from EEG, achieving high accuracy in a rodent model.

Findings

Achieved an AUC of 0.99 in EPG detection.

Effective prediction of epileptogenesis before seizures.

Demonstrated feasibility of early diagnosis from EEG data.

Abstract

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.