Design and Development of Multi-modal Intelligent Anesthesia Monitoring System

Beijing Chao Yang Hospital330 enrolled

Overview

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

Study Type

OBSERVATIONAL

Enrollment

330

Conditions

Anesthesia

Interventions

Multi-modal Intelligent Anesthesia Monitoring SystemDIAGNOSTIC_TEST

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

Eligibility

Sex: ALLMax age: 85 Years

Medical Language ↔ Plain English

Inclusion Criteria: 1. Age: 0-65 years old 2. ASA: Level I-III 3. Patients undergoing non cardiac surgery under general anesthesia 4. Informed consent of the patient or legal representative Exclusion Criteria: 1. Previous history of severe neurological disorders 2. History of mental illness and related medication use 3. Individuals who are unable to cooperate in completing cognitive function tests 4. Severe hearing or visual impairment 5. Preoperative delirium in patients 6. Individuals who have experienced severe adverse reactions such as cardiac arrest and cardiopulmonary resuscitation during surgery 7. Those who require neurosurgery, head and facial surgery 8. Individuals who are allergic to EEG and fNIRS electrodes

Locations (1)

Department of Anesthesiology, Beijing Chaoyang Hospital, Capital Medical University

Beijing, Beijing Municipality, China

Outcomes

Primary Outcomes

the depth of anesthesia (too deep or too shallow)

PRST score system, combined with BIS index for comprehensive judgment

Time frame: During general anesthesia

Secondary Outcomes

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

Spectral Analysis，Connectivity Analysis，Brain Networks Analysis

Time frame: During general anesthesia

Characteristics of perioperative neurovascular coupling

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.

Time frame: Perioperative

Data from ClinicalTrials.gov

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