Quantitative analysis of EEG reactivity for neurological prognostication after cardiac arrest,Clinical Neurophysiology

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Quantitative analysis of EEG reactivity for neurological prognostication after cardiac arrest
Clinical Neurophysiology ( IF 3.7 ) Pub Date : 2021-07-13 , DOI: 10.1016/j.clinph.2021.07.004
M M Admiraal ₁ , L A Ramos ₂ , S Delgado Olabarriaga ₃ , H A Marquering ₄ , J Horn ₅ , A F van Rootselaar ₁

Affiliation

Objective

To test whether 1) quantitative analysis of EEG reactivity (EEG-R) using machine learning (ML) is superior to visual analysis, and 2) combining quantitative analyses of EEG-R and EEG background pattern increases prognostic value for prediction of poor outcome after cardiac arrest (CA).

Methods

Several types of ML models were trained with twelve quantitative features derived from EEG-R and EEG background data of 134 adult CA patients. Poor outcome was a Cerebral Performance Category score of 3–5 within 6 months.

Results

The Random Forest (RF) trained on EEG-R showed the highest AUC of 83% (95-CI 80–86) of tested ML classifiers, predicting poor outcome with 46% sensitivity (95%-CI 40–51) and 89% specificity (95%-CI 86–92). Visual analysis of EEG-R had 80% sensitivity and 65% specificity. The RF was also the best classifier for EEG background (AUC 85%, 95%-CI 83–88) at 24 h after CA, with 62% sensitivity (95%-CI 57–67) and 84% specificity (95%-CI 79–88). Combining EEG-R and EEG background RF classifiers reduced the number of false positives.

Conclusions

Quantitative EEG-R using ML predicts poor outcome with higher specificity, but lower sensitivity compared to visual analysis of EEG-R, and is of some additional value to ML on EEG background data.

Significance

Quantitative EEG-R using ML is a promising alternative to visual analysis and of some added value to ML on EEG background data.

中文翻译：

脑电图反应性对心脏骤停后神经系统预后的定量分析

客观的

测试 1) 使用机器学习 (ML) 对 EEG 反应性 (EEG-R) 进行定量分析是否优于视觉分析，以及 2) 结合 EEG-R 和 EEG 背景模式的定量分析是否增加了预测不良结果的预后价值心脏骤停 (CA)。

方法

使用源自 134 名成年 CA 患者的 EEG-R 和 EEG 背景数据的 12 个定量特征训练了几种类型的 ML 模型。较差的结果是 6 个月内脑功能分类评分为 3-5。

结果

在 EEG-R 上训练的随机森林 (RF) 显示测试的 ML 分类器的最高 AUC 为 83% (95-CI 80-86)，预测不良结果的灵敏度为 46% (95%-CI 40-51) 和 89%特异性（95%-CI 86-92）。EEG-R 的视觉分析具有 80% 的敏感性和 65% 的特异性。RF 还是 CA 后 24 小时 EEG 背景（AUC 85%，95%-CI 83-88）的最佳分类器，灵敏度为 62%（95%-CI 57-67）和 84% 特异性（95%- CI 79-88）。结合 EEG-R 和 EEG 背景 RF 分类器减少了误报的数量。