Sedation Level on Machine Learning With Electroencephalogram in Painless Gastroenteroscopy Patients

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

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.

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.

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