Improved tracking of sevoflurane anesthetic states with drug-specific machine learning models

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dc.contributor.authorKashkooli, Kimia
dc.contributor.authorPolk, Sam L.
dc.contributor.authorHahm, Eunice Y.
dc.contributor.authorMurphy, James
dc.contributor.authorEthridge, Breanna R.
dc.contributor.authorGitlin, Jacob
dc.contributor.authorIbala, Reine
dc.contributor.authorMekonnen, Jennifer
dc.contributor.authorPedemonte, Juan C.
dc.contributor.authorSun, Haoqi
dc.contributor.authorWestover, M. Brandon
dc.contributor.authorBarbieri, Riccardo
dc.contributor.authorAkeju, Oluwaseun
dc.contributor.authorChamadia, Shubham
dc.date.accessioned2025-01-23T19:49:10Z
dc.date.available2025-01-23T19:49:10Z
dc.date.issued2020
dc.description.abstractObjective.The ability to monitor anesthetic states using automated approaches is expected to reduce inaccurate drug dosing and side-effects. Commercially available anesthetic state monitors perform poorly when ketamine is administered as an anesthetic-analgesic adjunct. Poor performance is likely because the models underlying these monitors are not optimized for the electroencephalogram (EEG) oscillations that are unique to the co-administration of ketamine.Approach.In this work, we designed twok-nearest neighbors algorithms for anesthetic state prediction.Main results.The first algorithm was trained only on sevoflurane EEG data, making it sevoflurane-specific. This algorithm enabled discrimination of the sevoflurane general anesthesia (GA) state from sedated and awake states (true positive rate = 0.87, [95% CI, 0.76, 0.97]). However, it did not enable discrimination of the sevoflurane-plus-ketamine GA state from sedated and awake states (true positive rate = 0.43, [0.19, 0.67]). In our second algorithm, we implemented a cross drug training paradigm by including both sevoflurane and sevoflurane-plus-ketamine EEG data in our training set. This algorithm enabled discrimination of the sevoflurane-plus-ketamine GA state from sedated and awake states (true positive rate = 0.91, [0.84, 0.98]).Significance.Instead of a one-algorithm-fits-all-drugs approach to anesthetic state monitoring, our results suggest that drug-specific models are necessary to improve the performance of automated anesthetic state monitors.
dc.description.funderNational Institutes of Health, National Institute of Aging
dc.fuente.origenWOS
dc.identifier.doi10.1088/1741-2552/ab98da
dc.identifier.eissn1741-2552
dc.identifier.issn1741-2560
dc.identifier.urihttps://doi.org/10.1088/1741-2552/ab98da
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/100485
dc.identifier.wosidWOS:000560065000001
dc.issue.numero4
dc.language.isoen
dc.revistaJournal of neural engineering
dc.rightsacceso restringido
dc.subjectanesthesia
dc.subjectelectroencephalogram
dc.subjectketamine
dc.subjectmachine learning
dc.subjectsevoflurane
dc.titleImproved tracking of sevoflurane anesthetic states with drug-specific machine learning models
dc.typeartículo
dc.volumen17
sipa.indexWOS
sipa.trazabilidadWOS;2025-01-12
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