Detecting Cerebral Ischemia from Electroencephalography During Carotid Endarterectomy Using Machine Learning

Intraoperative stroke is a major concern during high-risk surgical procedures such as carotid endarterectomy (CEA). Ischemia, a stroke precursor, can be detected using continuous electroencephalographic (cEEG) monitoring of electrical changes caused by changes in cerebral blood flow. However, monitoring by experts is currently resource-intensive and prone to error. We investigated if supervised machine learning (ML) could detect ischemia accurately using intraoperative cEEG. Using cEEG recordings from 802 patients, we trained six ML models, including naive Bayes, logistic regression, support vector classifier, random forest (RF), light gradient-boosting machine (LGBM), and eXtreme Gradient Boosting with random forest (XGBoost RF), and tested them on a validation dataset of 30 patients. Each cEEG recording in the validation dataset was labeled independently by five expert neurophysiologists who regularly perform intraoperative neuromonitoring. We did not derive consensus labels but rather evaluated an ML model in a pairwise fashion using one expert as a reference at a time, due to the experts variability in label determination, which is typical for clinical tasks. The tree-based ML models, including RF, LGBM, and XGBoost RF, performed best, with AUROC values ranging from 0.92 to 0.93 and AUPRC values ranging from 0.79 to 0.83. Our findings suggest that ML models can serve as the foundation for a real-time intraoperative monitoring system that can assist the neurophysiologist in monitoring patients.