Keene Jennifer C, Loe Maren E, Fulton Talie, Keene Maire, Morrissey Michael J, Tomko Stuart R, Vesoulis Zachary A, Zempel John M, Ching ShiNung, Guerriero Réjean M
Division of Pediatric & Developmental Neurology, Department of Neurology. Washington University in St. Louis, St. Louis, Missouri U.S.A.
Department of Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, Missouri, U.S.A.
J Clin Neurophysiol. 2025 Jan 1;42(1):57-63. doi: 10.1097/WNP.0000000000001067. Epub 2024 Jun 10.
Seizures occur in up to 40% of neonates with neonatal encephalopathy. Earlier identification of seizures leads to more successful seizure treatment, but is often delayed because of limited availability of continuous EEG monitoring. Clinical variables poorly stratify seizure risk, and EEG use to stratify seizure risk has previously been limited by need for manual review and artifact exclusion. The goal of this study is to compare the utility of automatically extracted quantitative EEG (qEEG) features for seizure risk stratification.
We conducted a retrospective analysis of neonates with moderate-to-severe neonatal encephalopathy who underwent therapeutic hypothermia at a single center. The first 24 hours of EEG underwent automated artifact removal and qEEG analysis, comparing qEEG features for seizure risk stratification.
The study included 150 neonates and compared the 36 (23%) with seizures with those without. Absolute spectral power best stratified seizure risk with area under the curve ranging from 63% to 71%, followed by range EEG lower and upper margin, median and SD of the range EEG lower margin. No features were significantly more predictive in the hour before seizure onset. Clinical examination was not associated with seizure risk.
Automatically extracted qEEG features were more predictive than clinical examination in stratifying neonatal seizure risk during therapeutic hypothermia. qEEG represents a potential practical bedside tool to individualize intensity and duration of EEG monitoring and decrease time to seizure recognition. Future work is needed to refine and combine qEEG features to improve risk stratification.
在患有新生儿脑病的新生儿中，癫痫发作的发生率高达40%。早期识别癫痫发作能使癫痫治疗更成功，但由于持续脑电图监测的可及性有限，往往会延迟。临床变量对癫痫发作风险的分层效果不佳，而此前脑电图用于癫痫发作风险分层受到人工审核和伪迹排除需求的限制。本研究的目的是比较自动提取的定量脑电图（qEEG）特征在癫痫发作风险分层中的效用。
我们对在单一中心接受治疗性低温治疗的中重度新生儿脑病新生儿进行了回顾性分析。对脑电图的前24小时进行自动伪迹去除和qEEG分析，比较用于癫痫发作风险分层的qEEG特征。
该研究纳入了150名新生儿，并将36名（23%）有癫痫发作的新生儿与无癫痫发作的新生儿进行了比较。绝对频谱功率对癫痫发作风险的分层效果最佳，曲线下面积在63%至71%之间，其次是脑电图范围的下限和上限、脑电图范围下限的中位数和标准差。在癫痫发作前一小时，没有特征具有明显更强的预测性。临床检查与癫痫发作风险无关。
在治疗性低温治疗期间，自动提取的qEEG特征在分层新生儿癫痫发作风险方面比临床检查更具预测性。qEEG是一种潜在的实用床边工具，可用于个性化脑电图监测的强度和持续时间，并减少癫痫发作识别时间。未来需要开展工作来优化和结合qEEG特征，以改善风险分层。
