{"title":"利用脑电图记录对麻醉深度进行基于机器学习的精确监测","authors":"Zhiyi Tu, Yuehan Zhang, Xueyang Lv, Yanyan Wang, Tingting Zhang, Juan Wang, Xinren Yu, Pei Chen, Suocheng Pang, Shengtian Li, Xiongjie Yu, Xuan Zhao","doi":"10.1007/s12264-024-01297-w","DOIUrl":null,"url":null,"abstract":"<p>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.</p>","PeriodicalId":19314,"journal":{"name":"Neuroscience bulletin","volume":"57 1","pages":""},"PeriodicalIF":5.9000,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Accurate Machine Learning-based Monitoring of Anesthesia Depth with EEG Recording\",\"authors\":\"Zhiyi Tu, Yuehan Zhang, Xueyang Lv, Yanyan Wang, Tingting Zhang, Juan Wang, Xinren Yu, Pei Chen, Suocheng Pang, Shengtian Li, Xiongjie Yu, Xuan Zhao\",\"doi\":\"10.1007/s12264-024-01297-w\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>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.</p>\",\"PeriodicalId\":19314,\"journal\":{\"name\":\"Neuroscience bulletin\",\"volume\":\"57 1\",\"pages\":\"\"},\"PeriodicalIF\":5.9000,\"publicationDate\":\"2024-09-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Neuroscience bulletin\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1007/s12264-024-01297-w\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"NEUROSCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Neuroscience bulletin","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s12264-024-01297-w","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"NEUROSCIENCES","Score":null,"Total":0}
Accurate Machine Learning-based Monitoring of Anesthesia Depth with EEG Recording
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.
期刊介绍:
Neuroscience Bulletin (NB), the official journal of the Chinese Neuroscience Society, is published monthly by Shanghai Institutes for Biological Sciences (SIBS), Chinese Academy of Sciences (CAS) and Springer.
NB aims to publish research advances in the field of neuroscience and promote exchange of scientific ideas within the community. The journal publishes original papers on various topics in neuroscience and focuses on potential disease implications on the nervous system. NB welcomes research contributions on molecular, cellular, or developmental neuroscience using multidisciplinary approaches and functional strategies. We feature full-length original articles, reviews, methods, letters to the editor, insights, and research highlights. As the official journal of the Chinese Neuroscience Society, which currently has more than 12,000 members in China, NB is devoted to facilitating communications between Chinese neuroscientists and their international colleagues. The journal is recognized as the most influential publication in neuroscience research in China.