Correlating EEG Dynamics With Consciousness Alteration Under Anesthesia

Abstract

This prospective observational study is designed to investigate and compare the dynamic features of whole-brain electroencephalogram (EEG) during the induction of unconsciousness using various anesthetic agents with distinct pharmacological mechanisms. The primary objective is to identify common, drug-agnostic EEG biomarkers of anesthetic depth and to develop a novel, universal assessment system that addresses the limitations of the currently prevalent Bispectral Index (BIS), which demonstrates variable sensitivity across different anesthetics. Approximately 250 adult patients (ASA I-II) scheduled for elective surgery under general anesthesia will be enrolled. Patients will undergo preoperative cognitive assessment prior to induction. During anesthesia induction, 32-channel EEG signals will be continuously recorded alongside BIS values and behavioral state assessments using the MOAA/S scale as the reference standard. Patients will receive one of the following intravenous anesthetics for induction: Propofol, Ciprofol, Remimazolam, Esketamine, or Fospropofol. Features will be extracted from the preprocessed EEG data. Statistical analyses will compare these features across drug groups and in relation to behavioral state transitions. Machine learning models (e.g., Random Forest) will then be trained to classify states of consciousness based on the extracted EEG features, with model performance validated against the behavioral gold standard. The study aims to establish a more robust and generalizable neurophysiological framework for monitoring anesthetic depth, potentially improving the precision and safety of clinical anesthesia management.

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