AI-Driven Electrographic Seizure Classification and Seizure Onset Detection Using Image- and Time-Series-Based Approaches.

ABSTRACT:

Objective

Manually distinguishing between seizure and non-seizure events in intracranial electroencephalography (iEEG) recordings is highly time-consuming. In this study, we explored AI-based approaches for electrographic seizure classification (ESC) and seizure onset detection (SOD) in treatment-resistant epilepsy patients. ESC involves distinguishing seizure events from non-seizure activity, while SOD focuses on pinpointing the exact moment a seizure begins.

Methods

We assessed several image-based and time-series-based model architectures for ESC and SOD, including convolutional neural networks (CNNs), vision transformers (ViTs), and time-series transformers. We used a dataset of approximately 560,000 iEEG traces from 291 focal epilepsy patients implanted with the NeuroPac

SUBMITTER: Afzal MF 

PROVIDER: S-EPMC12915893 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature