Accurate Machine Learning-based Monitoring of Anesthesia Depth with EEG Recording.
10.1007/s12264-024-01297-w
- Author:
Zhiyi TU
1
;
Yuehan ZHANG
1
;
Xueyang LV
1
;
Yanyan WANG
1
;
Tingting ZHANG
1
;
Juan WANG
1
;
Xinren YU
1
;
Pei CHEN
1
;
Suocheng PANG
1
;
Shengtian LI
2
;
Xiongjie YU
3
;
Xuan ZHAO
4
Author Information
1. Department of Anesthesiology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, 200072, China.
2. Bio-X Institutes, Key Laboratory for the Genetics of Development and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, 200240, China.
3. Department of Anesthesia, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, China. yuxiongj@gmail.com.
4. Department of Anesthesiology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, 200072, China. zhaoxuan301@tongji.edu.cn.
- Publication Type:Journal Article
- Keywords:
Anesthesia monitoring;
Electroencephalogram;
Ketamine;
Machine learning;
Propofol
- MeSH:
Animals;
Machine Learning;
Electroencephalography/methods*;
Ketamine/administration & dosage*;
Rats;
Male;
Propofol/administration & dosage*;
Rats, Sprague-Dawley;
Anesthesia, General/methods*;
Brain/physiology*;
Intraoperative Neurophysiological Monitoring/methods*
- From:
Neuroscience Bulletin
2025;41(3):449-460
- CountryChina
- Language:English
-
Abstract:
General anesthesia, pivotal for surgical procedures, requires precise depth monitoring to mitigate risks ranging from intraoperative awareness to postoperative cognitive impairments. Traditional assessment methods, relying on physiological indicators or behavioral responses, fall short of accurately capturing the nuanced states of unconsciousness. This study introduces a machine learning-based approach to decode anesthesia depth, leveraging EEG data across different anesthesia states induced by propofol and esketamine in rats. Our findings demonstrate the model's robust predictive accuracy, underscored by a novel intra-subject dataset partitioning and a 5-fold cross-validation method. The research diverges from conventional monitoring by utilizing anesthetic infusion rates as objective indicators of anesthesia states, highlighting distinct EEG patterns and enhancing prediction accuracy. Moreover, the model's ability to generalize across individuals suggests its potential for broad clinical application, distinguishing between anesthetic agents and their depths. Despite relying on rat EEG data, which poses questions about real-world applicability, our approach marks a significant advance in anesthesia monitoring.
