Alterations in coordinated EEG activity precede the development of seizures in comatose children

Objective: We aimed to test the hypothesis that computational features of the first several minutes of EEG recording can be used to estimate the risk for development of acute seizures in comatose critically-ill children. Methods: In a prospective cohort of 118 comatose children, we computed features of the first five minutes of artifact-free EEG recording (spectral power, inter-regional synchronization and cross-frequency coupling) and tested if these features could help identify the 25 children who went on to develop acute symptomatic seizures during the subsequent 48 hours of cEEG monitoring. Results: Children who developed acute seizures demonstrated higher average spectral power, particularly in the theta frequency range, and distinct patterns of inter-regional connectivity, characterized by greater connectivity at delta and theta frequencies, but weaker connectivity at beta and low gamma frequencies. Subgroup analyses among the 97 children with the same baseline EEG background pattern (generalized slowing) yielded qualitatively and quantitatively similar results. Conclusions: These computational features could be applied to baseline EEG recordings to identify critically-ill children at high risk for acute symptomatic seizures. Significance: If confirmed in independent prospective cohorts, these features would merit incorporation into a decision support system in order to optimize diagnostic and therapeutic management of seizures among comatose children. (c) 2021 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

Automated summary

The computational features of the first few minutes of EEG recording can be used to estimate the risk of acute seizures in comatose critically-ill children. Children who developed acute seizures showed higher spectral power in the theta frequency range, and distinct patterns of inter-regional connectivity. Future studies could incorporate these features into a decision support system for optimizing seizure management in comatose children.