Automated Seizure Classification Using Multimodal Large Language Models.

ABSTRACT:

Objective

Accurately distinguishing between epileptic seizures (ES) and nonepileptic seizures (NES) is a significant clinical challenge that typically requires resource-intensive inpatient video-EEG monitoring. Here, we developed a novel Multimodal Large Language Models (MLLMs)-based method for automated extraction of semiological features from videos of seizure events, and subsequently, classified the events as ES or NES.

Methods

90 videos of ES and NES events from 29 patients were obtained from an epilepsy monitoring unit at a large academic hospital. Events were labeled as ES or NES based on expert evaluation of video-EEG recordings and simultaneously annotated with 24 clinically relevant semiological features. We implemented a MLLMs framework that integrates open-source

SUBMITTER: Zhang L 

PROVIDER: S-EPMC12632642 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature