1. Early automated classification of neonatal hypoxic-ischemic encephalopathy - An aid to the decision to use therapeutic hypothermia.
- Author
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Lacan L, Betrouni N, Chaton L, Lamblin MD, Flamein F, Riadh Boukhris M, Derambure P, and Nguyen The Tich S
- Subjects
- Humans, Infant, Newborn, Female, Male, Machine Learning, Hypoxia-Ischemia, Brain therapy, Hypoxia-Ischemia, Brain physiopathology, Hypoxia-Ischemia, Brain classification, Hypoxia-Ischemia, Brain diagnosis, Hypothermia, Induced methods, Electroencephalography methods
- Abstract
Objective: 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., Methods: 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., Results: 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., Conclusions: 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., Significance: The proposed model has potential as a bedside clinical decision support tool for TH., (Copyright © 2024 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.)
- Published
- 2024
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