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Predicting Intraoperative Burst Suppression Using Preoperative EEG and Patient Characteristics.

Jingyi He¹, Joël M H Karel¹, Marcus L F Janssen²

¹Department of Advanced Computing Sciences, Maastricht University, 6211 LK Maastricht, The Netherlands.

International Journal of Neural Systems

|April 15, 2025

Summary

This summary is machine-generated.

This study developed an automated system to detect burst suppression (BS) during anesthesia using EEG. Machine learning models predict BS occurrence before surgery, improving patient safety and anesthesia management.

Keywords:

Electroencephalogram SHapley Additive exPlanations burst suppression general anesthesia prediction

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Area of Science:

Neuroscience
Anesthesiology
Biomedical Engineering

Background:

Burst suppression (BS) is an EEG pattern during general anesthesia linked to adverse patient outcomes.
Early detection and prediction of BS are crucial for optimizing anesthesia and enhancing patient safety.

Purpose of the Study:

To explore automatic detection of BS using intraoperative EEG.
To predict BS occurrence using preoperative EEG signals and patient characteristics.

Main Methods:

Utilized an EEG toolbox for automatic BS detection and annotation.
Employed five machine learning classifiers for BS prediction using preoperative data.
Applied techniques like SMOTE and feature enrichment for improved prediction accuracy.

Main Results:

Automatic BS detection tool achieved an accuracy of 0.75.
Initial BS prediction models showed modest performance (0.72 accuracy).
Final models (Random Forest, Gradient Boosting) achieved 0.86 accuracy and 0.94 ROC-AUC, identifying key predictive patient features.

Conclusions:

Automated BS detection and prediction are feasible and can be significantly improved with larger datasets and advanced machine learning.
Patient characteristics and preoperative EEG features are important indicators for predicting BS predisposition.