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

ABSTRACT:

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) and scalp EEG for different levels of sedation could increase the utility of EEG monitoring.

Methods

Simultaneous iEEG, scalp EEG, and Observer's Assessment of Alertness/Sedation (OAA/S) scores were recorded during emergence from anaesthesia in seven patients undergoing placement of intracranial electrodes for medically refractory epilepsy. A deep learning model was constructed to predict an OAA/S score of 0-2 vs 3-5 using iEEG, scalp EEG, and their combination. An additio

SUBMITTER: Han L 

PROVIDER: S-EPMC11867133 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature