Large Pre-trained EEG Model for Electrical Status Epilepticus during Sleep Detection.

Current diagnostic practices rely heavily on expert visual inspection of spike-and-slow-wave complexes in electroencephalogram (electroencephalogram) recordings.

This process is time-consuming, labor-intensive, and subjective, often resulting in poor inter-rater agreement.

To address these limitations, this paper introduces a novel ESES automatic detection framework based on a large pre-trained electroencephalogram model.

The high agreement between the model and expert annotation is confirmed by a Kappa coefficient of 0.8368 and Gwet's AC1 of 0.8394.