What question did this study set out to answer?

This research aims to identify nonconvulsive status epilepticus (NCSE) by analyzing functional connectivity using machine learning.

March 18, 2026Open Access

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

Key Points

This research aims to identify nonconvulsive status epilepticus (NCSE) by analyzing functional connectivity using machine learning.
Utilized graph-theoretical functional connectivity derived from EEG data
Compared NCSE patients with those in coma during the ictal-interictal continuum
Employed machine learning models for classification
Functional connectivity measures significantly differed between NCSE and coma-IIC patients
Machine learning models effectively distinguished between the two conditions

Abstract

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.

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Discussion

Authors

S.R. Ryu

Dong Ah Lee

Kang Min Park

Journals

encephalitis

Actions

Institutions

Inje University Haeundae Paik Hospital

References and Citations

Connected Papers

Building similarity graph...

Analyzing shared references across papers

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

Key Points

Abstract

Citation Network

Connected Papers

Discussion

Authors

Journals

Actions

Institutions

References and Citations

Citation Network

Connected Papers

Discussion

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