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Sedation Level on Machine Learning With Electroencephalogram in Painless Gastroenteroscopy Patients

Gastritis Clinical Trial

Official title:
Sedation Level Estimation Based on Machine Learning of Quantitative Occipital Electroencephalogram Features in Gastroenteroscopy Patients

Clinical Trial Summary

Painless endoscopy technology can make patients comfortable under anesthesia, but because of the painless inside.The diagnosis and treatment time of endoscopic examination is short, and the transport is fast. Anesthesia related wind such as deep breathing depression and hypoxemia will occurRisks.Eeg depth monitoring can assist anesthesiologists to evaluate the depth of anesthesia and reduce the risk. Artificial intelligence is adopted.There are few reports on the evaluation of anesthesia depth and drug dosage by electroencephalogram (EEG) monitoring in outpatient patients with painless gastroenteroscopy.

Clinical Trial Description

1.examination, and establish brainClassification model of electrogram and sedation depth;2. The EEG characteristics, basic information and propofol were administered to all patients.The dose model of propofol painless gastroenteroscopy was established by machine learning to guide anesthesia Drug use;3. Evaluate the time and space complexity of the model, build the model, and assist the anesthesiologist to individualize the patient Chemical, safe and comfortable management. ;

Study Design

Related Conditions & MeSH terms

Clinical Trial Details
NCT number NCT05545397
Study type Observational
Source General Hospital of Ningxia Medical University
Contact Huiwen Zhang
Phone 18709500998
Email zhanghuiwen1128@163.com
Status Recruiting
Phase
Start date September 15, 2022
Completion date December 31, 2022
View Details
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