Multidimensional Microstate Alterations and Interpretable Classification of Electrical Status Epilepticus During Sleep Using Multi-Channel EEG

Electrical status epilepticus during sleep (ESES) is an epilepsy syndrome requiring early diagnosis. Its diagnostic subjectivity and therapeutic inadequacy necessitate the urgent discovery of objective markers. This study developed a microstate-centered multidimensional EEG framework to distinguish epilepsy patients with ESES from those without ESES and to identify interpretable neurophysiological alterations. Broadband spatiotemporal parameters, frequency-specific microstate features, nonlinear complexity, symbolic-sequence dynamics, Spike-Wave Index (SWI) correlations, and exploratory classification performance were evaluated. ESES-related alterations were concentrated in microstate B and C, involving prolonged and more variable temporal persistence, altered occurrence frequency, increased global field strength, frequency-sensitive duration changes, and reduced frequency chaos game representation (FCGR) entropy. Markov order features were positively associated with SWI-related electrophysiological burden, suggesting potential severity-related sequence dynamics. A five-feature L2-regularized logistic regression model achieved promising exploratory discrimination, with ACC = 0.8571 and AUC = 0.8660. This study indicated that ESES is associated with more constrained and less flexible microstate dynamics, while a compact, interpretable microstate-derived model achieved promising exploratory discrimination. These findings may provide novel analytical perspectives for deciphering ESES pathophysiology and identifying candidate neurophysiological markers for future assessment.