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<p>Processed electroencephalography (EEG) indices used to guide anesthetic dosing in adults are not validated in young infants. Raw EEG can be processed mathematically, yielding quantitative EEG parameters (qEEG). We hypothesized that machine learning combined with qEEG can accurately classify expired sevoflurane concentrations in young infants. Knowledge from this may contribute to development of future infant-specific EEG algorithms. Frontal EEG collected from infants ≤ 3&#xa0;months were time-matched as one-minute epochs to expired sevoflurane (eSevo). Fifteen qEEG parameters were extracted from each epoch and eight machine learning models combined the qEEG to classify each epoch into one of four eSevo levels (%): 0.1–1.0, 1.0–2.1, 2.1–2.9, and &gt; 2.9. 64 epochs formed the p<i>ost hoc</i> SHAP dataset to determine the qEEG that contributed most to the model. The remaining epochs were randomly split 50 times into 80/20 training/testing sets. Accuracy and F1-score determined model performance. 42 infants provided 4574 epochs. The top classifiers K-nearest neighbors, default multi-layer perceptron, and support vector machine achieved 67.5–68.7% accuracy. Burst suppression ratio and entropy β were the top contributors to the models. Post hoc analysis performed without burst suppression ratio yielded similar prediction performance. In young infants, machine learning applied to qEEG predicted eSevo levels with moderate success. Burst suppression ratio, the most important contributor, represented an <i>efficient</i> EEG feature that encapsulated underlying EEG changes seen on other qEEG features. These results provided insight into EEG parameter selection and optimal machine learning models used for future development of infant-specific EEG algorithms.</p>

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Quantitative electroencephalogram and machine learning to predict expired sevoflurane concentration in infants

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月17日

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  • Rachit Kumar,
  • Justin Skowno,
  • Britta S. von Ungern-Sternberg,
  • Andrew Davidson,
  • Ting Xu,
  • Jianmin Zhang,
  • XingRong Song,
  • Mazhong Zhang,
  • Ping Zhao,
  • Huacheng Liu,
  • Yifei Jiang,
  • Yunxia Zuo,
  • Jurgen C. de Graaff,
  • Laszlo Vutskits,
  • Vanessa A. Olbrecht,
  • Peter Szmuk,
  • Allan F. Simpao,
  • Fuchiang Rich Tsui,
  • Jayant Nick Pratap,
  • Asif Padiyath,
  • Olivia Nelson,
  • Charles D. Kurth,
  • Ian Yuan,
  • Minal Menezes,
  • Suzette Sheppard,
  • David Sommerfield,
  • ZhengZheng Gao,
  • DongXu Lei,
  • Jijian Zheng,
  • Mengmeng Ding,
  • Panpan Chen,
  • Bin Du,
  • Abhusani Bhuju,
  • Camille van Hoorn,
  • Emilie Roden,
  • Jimmy W. Huh,
  • Shih-Shan Lang,
  • Paula Hu,
  • Rita Saynhalath,
  • Proshad Efune,
  • Gijo Alex

摘要

Processed electroencephalography (EEG) indices used to guide anesthetic dosing in adults are not validated in young infants. Raw EEG can be processed mathematically, yielding quantitative EEG parameters (qEEG). We hypothesized that machine learning combined with qEEG can accurately classify expired sevoflurane concentrations in young infants. Knowledge from this may contribute to development of future infant-specific EEG algorithms. Frontal EEG collected from infants ≤ 3 months were time-matched as one-minute epochs to expired sevoflurane (eSevo). Fifteen qEEG parameters were extracted from each epoch and eight machine learning models combined the qEEG to classify each epoch into one of four eSevo levels (%): 0.1–1.0, 1.0–2.1, 2.1–2.9, and > 2.9. 64 epochs formed the post hoc SHAP dataset to determine the qEEG that contributed most to the model. The remaining epochs were randomly split 50 times into 80/20 training/testing sets. Accuracy and F1-score determined model performance. 42 infants provided 4574 epochs. The top classifiers K-nearest neighbors, default multi-layer perceptron, and support vector machine achieved 67.5–68.7% accuracy. Burst suppression ratio and entropy β were the top contributors to the models. Post hoc analysis performed without burst suppression ratio yielded similar prediction performance. In young infants, machine learning applied to qEEG predicted eSevo levels with moderate success. Burst suppression ratio, the most important contributor, represented an efficient EEG feature that encapsulated underlying EEG changes seen on other qEEG features. These results provided insight into EEG parameter selection and optimal machine learning models used for future development of infant-specific EEG algorithms.