Anesthesia
Conditions
Keywords
Anesthesia monitoring, Near infrared reflectance spectroscopy（NIRS）, electroencephalogram（EEG）, multimodal, Age dependent, Machine learning
Brief summary
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.
Interventions
DIAGNOSTIC_TESTMulti-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)
Sponsors
Beijing Chao Yang Hospital
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
0 Years to 65 Years
Healthy volunteers
No
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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| the depth of anesthesia (too deep or too shallow) | During general anesthesia | PRST score system, combined with BIS index for comprehensive judgment |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| EEG characteristics of loss of consciousness induced by different general anesthesia drugs | During general anesthesia | Spectral Analysis，Connectivity Analysis，Brain Networks Analysis |
| Characteristics of perioperative neurovascular coupling | 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. |
Countries
China
Outcome results
None listed