Clinical trial NCT05545397: Sedation Level on Machine Learning With Electroencephalogram in Painless Gastroenteroscopy Patients
Official title: Sedation Level Estimation Based on Machine Learning of Quantitative Occipital Electroencephalogram Features in Gastroenteroscopy Patients
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Gastritis
Keywords: Deep Sedation
Machine learning
EEG characheristics;
Painless gastroscopy
Lead sponsor: General Hospital of Ningxia Medical University
Sponsor class: OTHER
First posted: 2022-09-19
Enrollment: 50
Sex: ALL
Minimum age (years): 18
Maximum age (years): 65
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Patients who were eligible for this study were monitored by EEG, and propol was pumped at a rate of 600ml/h，MOAA/S was used to evaluate the depth of anesthesia. EEG was labeled, and the amount of propofol was recorded.The MATLAB software was used to analyze the EEG features, establish the classifier and establish the painless gastroenteroscopy of propofol induction dosage prediction model.

Brief 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.

Detailed 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.

Eligibility criteria: Inclusion Criteria:

1. Painless gastroenteroscopy patients
2. ASA classification is Ⅰ-Ⅱ
3. Patients who are willing to cooperate
4. No schizophrenia,epilepsy and other neurological diseases

Exclusion Criteria:

1. allergic to propofol
2. Patients who are unwilling to cooperate
3. Patients with psychiatric disorders
4. Patients using sedative drugs for a long time
5. Patients with neuropsychiatric disorders
6. Patients with severe heart, liver, kidney and respiratory diseases

Primary outcomes:
measure: Electroencephalogram features
timeFrame: during gastroenteroscopy procedure
measure: Propofol dosage
timeFrame: during gastroenteroscopy procedure
Secondary outcomes:
measure: physiological parameter(blood pressure )
timeFrame: during gastroenteroscopy procedure
measure: physiological parameter(heart rate )
timeFrame: during gastroenteroscopy procedure
measure: physiological parameter(oxygen saturation )
timeFrame: during gastroenteroscopy procedure
measure: time parameter （Arousal times）
timeFrame: during endoscopy
measure: success rate of once endoscopy
timeFrame: during endoscopy
measure: Incidence of hypoxemia
timeFrame: during endoscopy
measure: Endoscopist satisfaction by questionnaire
timeFrame: during endoscopy
measure: vasoactive drugs
timeFrame: during endoscopy
Other outcomes:
Locations:
city: Yinchuan
country: China
facility: Ningxia Medical University Cardiovascular and Cerebrovascular Disease Hospital
state: The Ningxia Hui Autonomous Region
Study officials:
References: