ULR 2694 - METRICS, University of Lille, Faculty of Medicine, Avenue Eugène Avinée, Lille F-59000, France; Department of Pediatric Neurology, CHU Lille, Hôpital Roger Salengro, Rue Emile Laine, Lille F-59000, France.
INSERM U 1172, F-59000, University of Lille, Faculty of Medicine, 2 Avenue Eugène Avinée, Lille F-59000, France; Department of Clinical Neurophysiology, CHU Lille, Hôpital Roger Salengro, Rue Emile Laine, Lille F-59000, France.
Clin Neurophysiol. 2024 Oct;166:108-116. doi: 10.1016/j.clinph.2024.07.015. Epub 2024 Aug 2.
The study aimed to address the challenge of early assessment of neonatal hypoxic-ischemic encephalopathy (HIE) severity to identify candidates for therapeutic hypothermia (TH). The objective was to develop an automated classification model for neonatal EEGs, enabling accurate HIE severity assessment 24/7.
EEGs recorded within 6 h of life after perinatal anoxia were visually graded into 3 severity groups (HIE French Classification) and quantified using 6 qEEG markers measuring amplitude, continuity and frequency content. Machine learning models were developed on a dataset of 90 EEGs and validated on an independent dataset of 60 EEGs.
The selected model achieved an overall accuracy of 80.6% in the development phase and 80% in the validation phase. Notably, the model accurately identified 28 out of 30 children for whom TH was indicated after visual EEG analysis, with only 2 cases (moderate EEG abnormalities) not recommended for cooling.
The combination of clinically relevant qEEG markers led to the development of an effective automated EEG classification model, particularly suited for the post-anoxic latency phase. This model successfully discriminated neonates requiring TH.
The proposed model has potential as a bedside clinical decision support tool for TH.
本研究旨在解决新生儿缺氧缺血性脑病（HIE）严重程度的早期评估问题，以确定候选者进行治疗性低温（TH）。目的是开发一种新生儿脑电图的自动分类模型，实现 24/7 对 HIE 严重程度的准确评估。
在围产期缺氧后 6 小时内记录的脑电图通过视觉分级为 3 个严重程度组（HIE 法国分类），并使用 6 个 qEEG 标志物来量化振幅、连续性和频率内容。机器学习模型是在 90 个脑电图数据集上开发的，并在 60 个脑电图数据集上进行了验证。
所选模型在开发阶段的总体准确率为 80.6%，在验证阶段为 80%。值得注意的是，该模型准确地识别了 30 名接受 TH 治疗的儿童中的 28 名，只有 2 名（中度脑电图异常）不建议进行冷却。
临床相关 qEEG 标志物的组合导致了一种有效的自动脑电图分类模型的开发，特别适合于缺氧后潜伏期阶段。该模型成功区分了需要 TH 的新生儿。
该模型有望成为治疗性低温的床边临床决策支持工具。
