Identification of nonconvulsive status epilepticus in the ictal-interictal continuum using artificial intelligence: a prospective observational cohort study.

Purpose: This study aims to investigate differences in functional connectivity between patients on the ictal-interictal continuum (IIC) with nonconvulsive status epilepticus (NCSE) versus those with coma-IIC, and to evaluate whether machine learning based on these connectivity measures was able to distinguish between these two groups. Methods: We prospectively enrolled patients with IIC electroencephalography (EEG) patterns and classified them into NCSE or coma-IIC groups according to the Salzburg criteria and clinical information. We analyzed functional connectivity based on EEG using graph theory. For deep learning, EEG signals were transformed into time-frequency images using short-time Fourier transforms. We investigated differences in functional connectivity between the two groups. Results: We enrolled 72 patients on the IIC. Of the 72 patients, 53 patients had NCSE, and 19 had coma-IIC. Patients with NCSE had decreased global functional connectivity in all frequency bands compared to patients with coma-IIC. Global efficiency was significantly lower in the NCSE group than the coma-IIC group (e.g., gamma band, 0.258 vs 0.350; p = 0.001), and local efficiency was consistently decreased across all frequency bands in the NCSE group (all p ≤ 0.01). Machine learning based on these measures classified patients with NCSE and those with coma-IIC with an accuracy of 92.8%, while the accuracy of the convolutional neural network model to distinguish between them was 73.9%. Conclusion: We demonstrated that graph-theoretical functional connectivity derived from EEG data differs significantly between patients with NCSE and those with coma-IIC. Furthermore, our results confirm the feasibility of using machine learning models based on these connectivity measures to effectively distinguish between these two conditions.