Department of Biomedical Physiology and Kinesiology, Behavioural and Cognitive Neuroscience Institute, Simon Fraser University, Vancouver, British Columbia, Canada.
North York General Hospital and Department of Paediatrics, University of Toronto, Toronto, Canada.
Clin Neurophysiol. 2021 Jul;132(7):1505-1514. doi: 10.1016/j.clinph.2021.03.015. Epub 2021 Mar 31.
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.
我们旨在检验以下假说，即通过对昏迷危重症儿童脑电记录的最初几分钟的计算特征进行分析，是否可以预测急性癫痫发作的风险。
在一项前瞻性队列研究中，我们对 118 例昏迷的儿童进行研究，计算了前 5 分钟无伪影脑电记录的特征（频谱功率、区域间同步和跨频耦合），并测试这些特征是否可以帮助识别在随后的 48 小时 cEEG 监测中发展为急性症状性癫痫的 25 例儿童。
发生急性癫痫发作的儿童表现出更高的平均频谱功率，特别是在θ频带，以及不同的区域间连接模式，其特征是在δ和θ频带的连接更强，但在β和低γ频带的连接较弱。在具有相同基线 EEG 背景模式（广泛减速）的 97 例儿童中进行的亚组分析得出了定性和定量相似的结果。
这些计算特征可应用于基线 EEG 记录，以识别处于急性症状性癫痫发作高风险的危重症儿童。
如果在独立的前瞻性队列中得到证实，这些特征将值得纳入决策支持系统，以优化昏迷儿童的癫痫诊断和治疗管理。
