Accurate Machine Learning‑based Monitoring of Anesthesia Depth with EEG Recording  

作　　者：Zhiyi Tu Yuehan Zhang Xueyang Lv Yanyan Wang Tingting Zhang Juan Wang Xinren Yu Pei Chen Suocheng Pang Shengtian Li Xiongjie Yu Xuan Zhao 

机构地区：[1]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

出　　处：《Neuroscience Bulletin》2025年第3期449-460,共12页神经科学通报(英文版)

基　　金：supported by grants from the Shanghai Municipal Health Commission(2023ZDFC0203);the National Natural Science Foundation of China(32171044).

摘　　要：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 intrasubject 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.

关 键 词：ELECTROENCEPHALOGRAM PROPOFOL KETAMINE Machine learning Anesthesia monitoring 

分 类 号：R614[医药卫生—麻醉学]