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Updated: Nov 22, 2025
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Published on: June 21, 2024
Quantitative approach to early neonatal EEG visual analysis in hypoxic-ischemic encephalopathy severity: Bridging the
Laure Lacan1, Nacim Betrouni2, Marie-Dominique Lamblin3
1EA4489, Environnement périnatal et santé, Faculté de médecine, Université de Lille, 2 Avenue Eugène Avinée, 59120 Loos, France; Service de Neurologie pédiatrique, Hôpital Roger Salengro, CHRU de Lille, Avenue du Professeur Emile Laine, 59037, France.
Objectives:
To identify relevant quantitative parameters for early classification of neonatal hypoxic-ischemic encephalopathy (HIE) severity from conventional EEGs.
Methods:
Ninety EEGs, recorded in full-term infants within 6 h of life after perinatal hypoxia, were visually classified according to the French EEG classification into three groups of increasing HIE severity. Physiologically significant EEG features (signal amplitude, continuity and frequency content) were automatically quantified using different parameters. The EEG parameters selection was based on their ability to reproduce the visual EEG classification. Post hoc analysis based on clinical outcome was performed.
Results:
Six EEG parameters were selected, with overall EEG classification performances between 61% and 70%. All parameters differed significantly between group 3 (severe) and groups 1 (normal-mildly abnormal) and 2 (moderate) EEGs (p < 0.001). Amplitude and discontinuity parameters were different between the 3 groups (p < 0.01) and were also the best predictors of clinical outcome. Conversely, pH and lactate did not differ between groups.
Discussion:
This study provides quantitative EEG parameters that are complementary to visual analysis as early markers of neonatal HIE severity. These parameters could be combined in a multiparametric algorithm to improve their classification performance. The absence of relationship between pH lactate and HIE severity reinforces the central role of early neonatal EEG.
