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Detecting Cerebral Ischemia From Electroencephalography During Carotid Endarterectomy Using Machine Learning
Amir I Mina1, Jessi U Espino1, Allison M Bradley1
1Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA.
Summary
Machine learning (ML) models show promise for detecting intraoperative ischemia during surgery, offering a more accurate alternative to human monitoring. This advancement in electroencephalography (EEG) analysis can improve patient safety and reduce healthcare costs.
Area of Science:
- Neuroscience
- Medical Technology
- Artificial Intelligence
Background:
- Cerebral ischemia detection during surgery using electroencephalography (EEG) is crucial for preventing stroke.
- Current neurophysiologist-based monitoring is susceptible to errors, necessitating improved diagnostic methods.
Purpose of the Study:
- To evaluate the efficacy of machine learning (ML) models for accurate and efficient intraoperative ischemia detection.
- To compare the performance of ML models against human neurophysiologists in identifying cerebral ischemia.
Main Methods:
- Supervised ML models were trained on a dataset of 802 patients with intraoperative ischemia.
- Models were validated on a separate dataset of 30 patients, with labels refined by five neurophysiologists.
- Performance metrics included sensitivity, specificity, Cohen's kappa, AUROC, and AUPRC.
Main Results:
- Neurophysiologist agreement showed moderate-to-substantial agreement (kappa: 0.59–0.74).
- ML models achieved comparable sensitivity (63–89%) and specificity (85–96%) to neurophysiologists (58–93% and 83–96%, respectively).
- Random Forest, LightGBM, and XGBoost demonstrated high performance with AUROC values of 0.92–0.93 and AUPRC values of 0.79–0.83.
Conclusions:
- ML models offer a reliable and potentially more accurate approach to intraoperative ischemia monitoring.
- Implementing ML can enhance patient safety during surgery by improving stroke precursor detection.
- This technology has the potential to reduce healthcare costs associated with monitoring and stroke complications.
