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

• Published in: Clinical neurophysiology 2021-07, Vol.132 (7), p.1505-1514
• Main Authors: Vakorin, Vasily A., Nita, Dragos A., Payne, Eric T., McBain, Kristin L., Frndova, Helena, Go, Cristina, Ribary, Urs, Abend, Nicholas S., Gallentine, William B., Nash, Kendall B., Hutchison, James S., Parshuram, Christopher S., Snead, O. Carter, van Straaten, Ilse E.C.W., Stam, Cornelis J., Doesburg, Sam M., Hahn, Cecil D.
• Format: Article
• Language: English
• Subjects: Brain connectivity; Coma; Critically ill children; Electroencephalography (EEG); ICU
• ISSN: 1388-2457; 1872-8952
• DOI: 10.1016/j.clinph.2021.03.015

•Comatose critically-ill children are at risk for seizures, often being electrographic-only, without accompanying clinical signs.•EEG features of the first 5 minutes of critical care EEG recordings differed among children who went on to develop acute seizures.•If validated in other cohorts, EEG features may be an effective seizure risk stratification tool for critically-ill children. 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. 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. 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. These computational features could be applied to baseline EEG recordings to identify critically-ill children at high risk for acute symptomatic seizures. 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.