Correlating EEG Dynamics With Consciousness Alteration Under Anesthesia

EEG Signatures of Unconsciousness Induced by Anesthetic Agents and Their Dynamics Across the Consciousness Continuum

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.

Study Overview

• Status: Recruiting
• Conditions: General Anesthetics; Altered State of Consciousness
• Intervention / Treatment: Drug: Anesthetic Agents

Detailed Description

The aim of the study is to identify and validate common whole-brain EEG biomarkers that accurately track the transition between conscious states (wakefulness, sedation, unconsciousness) across five intravenous anesthetics with distinct mechanisms of action: Propofol, Ciprofol, Remimazolam, Esketamine, and Fospropofol.

Design:

This is a single-center, prospective, observational cohort study. Consecutive eligible patients will be enrolled and grouped based on the clinical choice of anesthetic drug used for induction of general anesthesia. Data analysis will be performed by researchers blinded to the group allocation during the feature extraction and model development phases.

Approximately 250 adult patients (aged ≥18 years) scheduled for elective surgery under general anesthesia at Tongji Hospital, Wuhan, China, will be recruited between April 2026 and December 2027. Participants must have an ASA physical status of I or II, normal cognitive function (MMSE score ≥24), and provide written informed consent.

Interventions and Procedures:

All procedures represent standard clinical care; no experimental interventions are administered.

1. Preoperative Assessment: Demographics, medical history, and MMSE score will be recorded.
2. EEG and Behavioral Data Acquisition: During anesthesia induction, 32-channel EEG will be continuously recorded using a Greentek system with electrodes placed according to the international 10-20 system. Simultaneously, the BIS value (sensor placed infraorbitally)[referrence] and the patient's behavioral state will be recorded every 30 seconds using the Modified Observer's Assessment of Alertness/Sedation (MOAA/S) scale as the reference standard and the absence of the pupillary light reflex. Based on these behavioral responses, the depth of anesthesia will be categorized into three stages: sedation, adequate anesthesia depth, and deep anesthesia.
3. Anesthetic Protocol: Induction of anesthesia will be performed by the attending anesthesiologist in accordance with standard institutional practice. One of the five study drugs will be administered as an intravenous bolus, with the induction dose maintained via continuous infusion for 5 minutes.

Data Processing and Analysis

1. Data Curation: Data will be checked for quality, and epochs with artifacts or missing clinical data will be excluded.
2. EEG Feature Extraction: Pre-processed EEG data will be analyzed to extract features including but not limited to power spectral density, permutation entropy, phase-lag entropy, and functional connectivity metrics.
3. Data Analysis: During the induction of unconsciousness with five distinct general anesthetic agents, EEG biomarkers corresponding to transitions between three behavioral states-sedation, adequate anesthesia depth, and deep anesthesia-will be identified. The performance of these biomarkers in tracking depth of anesthesia will be quantitatively compared against that of the Bispectral Index (BIS).
4. Machine Learning Modeling: The dataset will be split into training (70%) and validation (30%) sets. A tree-based ensemble model (e.g., Random Forest) will be trained to classify consciousness states based on EEG features. Model performance will be evaluated using AUC, accuracy, precision, and cross-validation.

• Study Type: Observational
• Enrollment (Estimated): 250

Contacts and Locations

• Study Contact: Name: Hua Zheng, MD. & PhD.; Phone Number: 0086-27-83663173; Email: hzheng@hust.edu.cn

Study Locations

• China
  • Hubei
    • Wuhan, Hubei, China, 430030
      • Recruiting
      • Department of Anaesthesiology, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology
      • Contact: Hua Zheng, Dr.; Phone Number: 0086-27-83663173; Email: hzheng@hust.edu.cn

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

Patients scheduled for elective surgery requiring general anesthesia

Description

Inclusion Criteria:

1. Age ≥ 18 years.
2. Scheduled to undergo elective surgery requiring general anesthesia with endotracheal intubation or laryngeal mask airway (LMA).
3. American Society of Anesthesiologists (ASA) Physical Status Class I to III.
4. Preoperative Mini-Mental State Examination (MMSE) score ≥ 24, indicating normal cognitive function.
5. Body mass index (BMI) ≤ 30 kg/m².
6. Ability to understand the study and provide written informed consent.

Exclusion Criteria:

1. History of drug abuse or dependence.
2. Known major neurological disorders (e.g., epilepsy, stroke, neurodegenerative diseases).
3. History of major psychiatric disorders.
4. Known or suspected pregnancy.
5. Inability to provide informed consent due to cognitive impairment, language barrier, or any other reason.

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
General Anesthesia; Adult patients requiring general anesthesia for surgical procedures	Drug: Anesthetic Agents; Loss of consciousness was induced in humans using five distinct general anesthetic agents: propofol, ciprofol, remimazolam, esketamine, and fospropofol

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Identification of EEG Biomarkers for Sedation, Adequate Anesthesia, and Deep Anesthesia During Induction	Identification of two or more EEG biomarkers associated with three distinct levels of consciousness (sedation, adequate anesthesia depth, and deep anesthesia) during the induction of general anesthesia.	From 2026.02.20 to 2026.12.31
Identification of Common EEG Biomarkers of Consciousness Across Anesthetic Agents	dentification of common EEG biomarkers of consciousness that are consistent across two or more anesthetic agents with differing pharmacological mechanisms.	From 2026.02.20 to 2026.12.31

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Classification Performance of Novel EEG Biomarkers Compared to BIS for Assessing Anesthesia Depth	1. Comparison of the precision (hit rate) in classifying the three consciousness states sedation, adequate anesthesia depth, and deep anesthesia) between the newly identified EEG biomarkers and the Bispectral Index (BIS) for one or more anesthetic agents.	From 2027.01.01 to 2027.10.31
Development and Validation of a Mechine learning Model	Development and validation of a tree-based ensemble machine learning model using 70% of the dataset for training and 30% for testing, including evaluation of its accuracy and the area under the receiver operating characteristic curve (AUC-ROC)	From 2027.01.01 to 2027.10.31

Collaborators and Investigators

• Sponsor: Huazhong University of Science and Technology
• Investigators: Principal Investigator: Pu Zhou, PhD., Huazhong University of Science and Technology

Publications and helpful links

General Publications

• Liang Z, Tang B, Chang Y, Wang J, Li D, Li X, Wei C. State-related Electroencephalography Microstate Complexity during Propofol- and Esketamine-induced Unconsciousness. Anesthesiology. 2024 May 1;140(5):935-949. doi: 10.1097/ALN.0000000000004896.

Study record dates

Study Major Dates

• Study Start (Actual): February 10, 2026
• Primary Completion (Estimated): October 20, 2026
• Study Completion (Estimated): February 20, 2027

Study Registration Dates

• First Submitted: February 8, 2026
• First Submitted That Met QC Criteria: February 8, 2026
• First Posted (Actual): February 13, 2026

Study Record Updates

• Last Update Posted (Actual): March 24, 2026
• Last Update Submitted That Met QC Criteria: March 22, 2026
• Last Verified: February 1, 2026

More Information

Terms related to this study

• Keywords: Electroencephalography; General Anesthetics; Neural correlates of consciousness; Consciousness monitoring
• Additional Relevant MeSH Terms: Physiological Effects of Drugs; Central Nervous System Depressants; Pharmacologic Actions; Chemical Actions and Uses; Therapeutic Uses; Central Nervous System Agents; Anesthetics
• Other Study ID Numbers: TJ-IRB202601004

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: UNDECIDED

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No