Large Pre-trained EEG Model for Electrical Status Epilepticus during Sleep Detection.
TL;DR
A novel ESES automatic detection framework based on a large pre-trained EEG model that can automatically quantify spike-and-slow-wave complexes and calculate the spike-wave index (SWI) for ESES diagnosis and provides an efficient and reliable tool for automatic ESES detection.
Abstract
Electrical status epilepticus during sleep (ESES) is a severe but often underdiagnosed epileptic encephalopathy that can significantly impair cognitive development in children. Current diagnostic practices rely heavily on expert visual inspection of spike-and-slow-wave complexes in electroencephalogram (EEG) 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 EEG model. The framework employs a pre-trained NeuroEncoder to extract temporal and frequency features from spike-and-slow-wave complexes, and uniquely incorporates a temporal and spatial embedding module to enhance the model's ability to recognize spatiotemporal patterns. By leveraging the multi-head attention mechanism of stacked Transformers, the framework further captures complex relationships and deep features. A symmetric label smoothing cross-entropy is introduced, which applies both forward and backward constraints to reduce overfitting to noisy labels and improve robustness to label noise and individual variability. The proposed framework can automatically quantify spike-and-slow-wave complexes and calculate the spike-wave index (SWI) for ESES diagnosis. Evaluation was conducted on EEG recordings from 35 patients using a leave-one-subject-out cross-subject strategy. The results demonstrate excellent performance, with an accuracy of 91.91%, sensitivity of 91.52%, and specificity of 92.37%. 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. The proposed method demonstrates superior generalization across subjects and outperforms existing approaches. The proposed framework provides an efficient and reliable tool for automatic ESES detection, supporting early intervention and improved clinical management for patients with ESES.