Accurate Machine Learning-based Monitoring of Anesthesia Depth with EEG Recording
Zhiyi Tu1 · Yuehan Zhang1 · Xueyang Lv1 · Yanyan Wang1 · Tingting Zhang1 · Juan Wang1 · Xinren Yu1 · Pei Chen1 · Suocheng Pang1 · Shengtian Li4 · Xiongjie Yu2,3 · Xuan Zhao11 Department of Anesthesiology, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai 200072, China
2 Department of Anesthesia, Women’s Hospital, Zhejiang University School of Medicine, Hangzhou 310006, China
3 Zhejiang Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Women’s Hospital, Zhejiang University School of Medicine, Hangzhou 310027, China
4 Bio-X Institutes, Key Laboratory for the Genetics of Development and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai 200240, China
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.
Keywords
Electroencephalogram; Propofol; Ketamine; Machine learning; Anesthesia monitoring
