Machine learning to detect intraoperative ischemia from electroencephalography in carotid endarterectomy surgery.

Cerebral ischemia is a significant concern during high-risk surgeries, such as carotid endarterectomy (CEA). Continuous electroencephalography, monitored by neurophysiological experts, is used to detect cerebral ischemia during surgery; however, real-time visual interpretation is resource-intensive and error-prone. We evaluated machine learning (ML) models, including random forest (RF), eXtreme Gradient Boosting with a random forest base classifier (XGB), elastic-net logistic regression (LR), support vector classifier (SVC) with a radial basis function kernel, and naive Bayes (NB) classifier, for automated detection of cerebral ischemia during CEA using quantitative electroencephalographic (qEEG) features. RF achieved the highest sensitivity (0.79-0.83) and an area under the precision-recall curve (AUPRC) of 0.44, while XGB demonstrated the highest specificity (0.93-0.96) with an AUPRC of 0.36. Both models showed high negative predictive values and high area under the receiver operating characteristic (AUROC) scores. Feature-importance analysis identified alpha-band activity and hemispheric asymmetry as the most discriminative qEEG predictors of ischemia. These results highlight the potential of ML-assisted monitoring to support neurophysiology experts and enhance patient safety during high-risk surgical procedures.