Design and Development of Multi-modal Intelligent Anesthesia Monitoring System

This project integrates the characteristics of electroencephalo-graph（EEG）, cerebral oxygen, blood pressure, heart rate, etc., based on nonlinear theory and multi-modal monitoring system suitable for domestic patients, taking into neural oscillation, large sample data and machine learning theory, to develop a account changes in sedation, analgesia, cerebral hemodynamics and other factors, regardless of patient age and type of general anesthesia drugs.

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Key information

Conditions

Anesthesia

Age range

0 year–65 year

Sex eligibility

All sexes

Study type

Observational

Primary location

Department of Anesthesiology, Beijing Chaoyang Hospital, Capital Medical University

Beijing, Beijing Municipality, 100020, China

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

Age: 0-65 years old
ASA: Level I-III
Patients undergoing non cardiac surgery under general anesthesia
Informed consent of the patient or legal representative

Exclusion criteria

Previous history of severe neurological disorders
History of mental illness and related medication use
Individuals who are unable to cooperate in completing cognitive function tests
Severe hearing or visual impairment
Preoperative delirium in patients
Individuals who have experienced severe adverse reactions such as cardiac arrest and cardiopulmonary resuscitation during surgery
Those who require neurosurgery, head and facial surgery
Individuals who are allergic to EEG and fNIRS electrodes

Treatment and study plan

Multi-modal Intelligent Anesthesia Monitoring System

Diagnostic Test

To evaluate the sensitivity and specificity of self-developed anesthesia monitoring systems in diagnosing the depth of anesthesia (too deep or too shallow)

Primary outcomes

the depth of anesthesia (too deep or too shallow)

Time frame: During general anesthesia

PRST score system, combined with BIS index for comprehensive judgment

Secondary outcomes

EEG characteristics of loss of consciousness induced by different general anesthesia drugs

Time frame: During general anesthesia

Spectral Analysis，Connectivity Analysis，Brain Networks Analysis
Characteristics of perioperative neurovascular coupling

Time frame: Perioperative

EEG power and entropy indexes are extracted by moving window method as new time series, and a new time series consistent with NIRS is constructed. The entropy and power of different frequency bands after resampling were used as the indexes of neural activity, and ΔHbO and ΔHb were selected as the indexes of hemodynamic activity. The neurovascular coupling was evaluated by calculating the coherence of neural activity and hemodynamic activity.

Sponsors and collaborators

Lead sponsor

Beijing Chao Yang Hospital

Other

Registry information

Important dates

Study start: 2024
Primary completion: 2026
Study completion: 2026
First posted: Mar 19, 2024
Registry last updated: Sep 8, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

Published trials that share one or more normalized conditions with this study.

Completed