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 naïve 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. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement Research reported in this publication was supported by the National Institutes of Health under award number T32 GM008208 from the National Institute of General Medical Sciences, T15 LM007059 from the National Library of Medicine, and UL1 TR001857 from the National Center for Advancing Translational Sciences. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: IRB of the University of Pittsburgh gave ethical approval for this work. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present study are available upon reasonable request to the authors, consistent with IRB guidelines and data protection regulations.