Electrical status epilepticus in 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 [Formula: see text] and [Formula: see text]. 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.