Drug-Specific Models Improve the Performance of an EEG-based Automated Brain-State Prediction System

• Published in: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) Vol. 2019; pp. 5808 - 5811
• Main Authors: Kashkooli, Kimia, Murphy, James M., Sun, Haoqi, Westover, M. Brandon, Akeju, Oluwaseun, Polk, Sam L., Chamadia, Shubham, Hahm, Eunice, Ethridge, Breanna, Gitlin, Jacob, Ibala, Reine, Mekonnen, Jennifer, Pedemonte, Juan
• Format: Conference Proceeding Journal Article
• Language: English
• Published: United States IEEE 01.07.2019
• Subjects: Anesthesia, General; Anesthetics, Inhalation; Brain; Brain modeling; Data models; Drugs; Electroencephalography; Humans; Methyl Ethers; Monitoring; Oscillators; Predictive models
• ISSN: 1557-170X; 1558-4615; 2694-0604
• DOI: 10.1109/EMBC.2019.8856935

Maintaining anesthetic states using automated brain-state prediction systems is expected to reduce drug overdosage and associated side-effects. However, commercially available brain-state monitoring systems perform poorly on drug-class combinations. We assume that current automated brain-state prediction systems perform poorly because they do not account for brain-state dynamics that are unique to drug-class combinations. In this work, we develop a k-nearest neighbors model to test whether improvements to automated brain-state prediction of drug-class combinations are feasible. We utilize electroencephalogram data collected from human subjects who received general anesthesia with sevoflurane and general anesthesia with the drug-class combination of sevoflurane-plus-ketamine. We demonstrate improved performance predicting anesthesia-induced brain-states using drug-specific models.