Design and Development of Multi-modal Intelligent Anesthesia Monitoring System

Anesthesia Clinical Trial

Official title:

Design and Development of Multi-modal Intelligent Anesthesia Monitoring System

Clinical Trial Details — Status: Not yet recruiting

Administrative data

• NCT number: NCT06317025
• Other study ID #: SHOUFA2022-2Z-2039
• Status: Not yet recruiting
• Start date: May 2024
• Est. completion date: December 2025

Study information

• Verified date: March 2024
• Source: Beijing Chao Yang Hospital
• Contact: Changwei Wei
• Phone: 13810678936
• Email: changwei.wei[@]ccmu.edu.cn
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

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.

Recruitment information / eligibility

• Status: Not yet recruiting
• Enrollment: 330
• Est. completion date: December 2025
• Est. primary completion date: December 2025
• Accepts healthy volunteers: No
• Gender: All
• Age group: N/A to 85 Years

Eligibility

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

Related Conditions & MeSH terms

• Anesthesia

Intervention

Diagnostic Test:
• Multi-modal Intelligent Anesthesia Monitoring System
To evaluate the sensitivity and specificity of self-developed anesthesia monitoring systems in diagnosing the depth of anesthesia (too deep or too shallow)

Locations

Country	Name	City	State
n/a

Sponsors (1)

Lead Sponsor	Collaborator
Beijing Chao Yang Hospital

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	the depth of anesthesia (too deep or too shallow)	PRST score system, combined with BIS index for comprehensive judgment	During general anesthesia
Secondary	EEG characteristics of loss of consciousness induced by different general anesthesia drugs	Spectral Analysis,Connectivity Analysis,Brain Networks Analysis	During general anesthesia
Secondary	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 ?HbO and ?Hb were selected as the indexes of hemodynamic activity. The neurovascular coupling was evaluated by calculating the coherence of neural activity and hemodynamic activity.	Perioperative