Early automated classification of neonatal hypoxic-ischemic encephalopathy − An aid to the decision to use therapeutic hypothermia

IF 3.7 3区 医学 Q1 CLINICAL NEUROLOGY Clinical Neurophysiology Pub Date : 2024-08-02 DOI:10.1016/j.clinph.2024.07.015
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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.

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新生儿缺氧缺血性脑病的早期自动分类--辅助治疗性低温疗法的决策。
研究目的该研究旨在解决早期评估新生儿缺氧缺血性脑病(HIE)严重程度以确定治疗性低温(TH)候选者这一难题。研究目的是为新生儿脑电图建立一个自动分类模型,以便全天候准确评估缺氧缺血性脑病的严重程度:围产期缺氧后 6 小时内记录的脑电图被目测分为 3 个严重程度组(HIE 法国分类),并使用测量振幅、连续性和频率内容的 6 个 qEEG 标记进行量化。在一个包含 90 个脑电图的数据集上开发了机器学习模型,并在一个包含 60 个脑电图的独立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%,在验证阶段的准确率为 80%。值得注意的是,在对 30 名儿童进行可视脑电图分析后,该模型准确识别了其中 28 名儿童的 TH 指征,只有 2 例(中度脑电图异常)不建议降温:结合临床相关的 qEEG 标记,开发出了一种有效的自动脑电图分类模型,尤其适用于缺氧后的潜伏期。该模型成功区分了需要 TH 的新生儿:意义:所提出的模型有望成为 TH 的床旁临床决策支持工具。
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来源期刊
Clinical Neurophysiology
Clinical Neurophysiology 医学-临床神经学
CiteScore
8.70
自引率
6.40%
发文量
932
审稿时长
59 days
期刊介绍: As of January 1999, The journal Electroencephalography and Clinical Neurophysiology, and its two sections Electromyography and Motor Control and Evoked Potentials have amalgamated to become this journal - Clinical Neurophysiology. Clinical Neurophysiology is the official journal of the International Federation of Clinical Neurophysiology, the Brazilian Society of Clinical Neurophysiology, the Czech Society of Clinical Neurophysiology, the Italian Clinical Neurophysiology Society and the International Society of Intraoperative Neurophysiology.The journal is dedicated to fostering research and disseminating information on all aspects of both normal and abnormal functioning of the nervous system. The key aim of the publication is to disseminate scholarly reports on the pathophysiology underlying diseases of the central and peripheral nervous system of human patients. Clinical trials that use neurophysiological measures to document change are encouraged, as are manuscripts reporting data on integrated neuroimaging of central nervous function including, but not limited to, functional MRI, MEG, EEG, PET and other neuroimaging modalities.
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