Deep learning models using intracranial and scalp EEG for predicting sedation level during emergence from anaesthesia

Deep learning models using intracranial and scalp EEG for predicting sedation level during emergence from anaesthesia

Background: Maintaining an appropriate depth of anaesthesia is important for avoiding adverse effects from undermedication or overmedication during surgery. Electroencephalography (EEG) has become increasingly used to achieve this balance. Investigating the predictive power of intracranial EEG (iEEG...

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Bibliographic Details
Main Authors: Lichy Han, David A. Purger, Sarah L. Eagleman, Casey H. Halpern, Vivek Buch, Samantha M. Gaston, Babak Razavi, Kimford Meador, David R. Drover
Format: Article
Language:English
Published: Elsevier 2024-12-01
Series:BJA Open
Subjects:
deep learning
depth of sedation
electroencephalography
intracranial electroencephalography
prediction modelling
Online Access:http://www.sciencedirect.com/science/article/pii/S2772609624000911
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