脑电图功率谱的非周期分量反映麻醉的催眠水平。

IF 9.1 1区医学 Q1 ANESTHESIOLOGY

British journal of anaesthesia Pub Date : 2025-02-01 DOI:10.1016/j.bja.2024.09.027

Sandra Widmann , Julian Ostertag , Sebastian Zinn , Stefanie Pilge , Paul S. García , Stephan Kratzer , Gerhard Schneider , Matthias Kreuzer

{"title":"脑电图功率谱的非周期分量反映麻醉的催眠水平。","authors":"Sandra Widmann , Julian Ostertag , Sebastian Zinn , Stefanie Pilge , Paul S. García , Stephan Kratzer , Gerhard Schneider , Matthias Kreuzer","doi":"10.1016/j.bja.2024.09.027","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><div>Aperiodic (nonoscillatory) electroencephalogram (EEG) activity can be characterised by its power spectral density, which decays according to an inverse power law. Previous studies reported a shift in the spectral exponent <em>α</em> from consciousness to unconsciousness. We investigated the impact of aperiodic EEG activity on parameters used for anaesthesia monitoring to test the hypothesis that aperiodic EEG activity carries information about the hypnotic component of general anaesthesia.</div></div><div><h3>Methods</h3><div>We used simulated noise with varying inverse power law exponents <em>α</em> and the aperiodic component of EEGs recorded during wakefulness (<em>n</em>=62) and maintenance of general anaesthesia (<em>n</em>=125) in a diverse sample of surgical patients receiving sevoflurane, desflurane, or propofol, extracted using the Fitting Oscillations and One-Over-F algorithm. Four spectral EEG parameters (beta ratio, spectral edge frequency 95, spectral entropy, and alpha-to-delta ratio) and two time-series parameters (approximate [ApEn] and permutation entropy [PeEn]) were calculated from the simulated signals and human EEG data. Performance in distinguishing between consciousness and unconsciousness was evaluated with AUC values.</div></div><div><h3>Results</h3><div>We observed an increase in the spectral exponent from consciousness to unconsciousness (AUC=0.98 (0.94–1)). The spectral parameters exhibited linear or nonlinear responses to changes in <em>α</em>. Using aperiodic EEG activity instead of the entire spectrum for spectral parameter calculation improved the separation between consciousness and unconsciousness for all parameters (AUC<sub><em>aperiodic</em></sub>=0.98 (0.94–1.00) <em>vs</em> AUC<sub><em>original</em></sub>=0.71 (0.62–0.79) to AUC<sub><em>original</em></sub>=0.95 (0.92–0.98)) up to the level of ApEn (AUC=0.96 (0.93–0.98)) and PeEn (AUC=0.94 (0.90–0.97)).</div></div><div><h3>Conclusions</h3><div>Aperiodic EEG activity could improve discrimination between consciousness and unconsciousness using spectral analyses.</div></div>","PeriodicalId":9250,"journal":{"name":"British journal of anaesthesia","volume":"134 2","pages":"Pages 392-401"},"PeriodicalIF":9.1000,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11775845/pdf/","citationCount":"0","resultStr":"{\"title\":\"Aperiodic component of the electroencephalogram power spectrum reflects the hypnotic level of anaesthesia\",\"authors\":\"Sandra Widmann , Julian Ostertag , Sebastian Zinn , Stefanie Pilge , Paul S. García , Stephan Kratzer , Gerhard Schneider , Matthias Kreuzer\",\"doi\":\"10.1016/j.bja.2024.09.027\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Background</h3><div>Aperiodic (nonoscillatory) electroencephalogram (EEG) activity can be characterised by its power spectral density, which decays according to an inverse power law. Previous studies reported a shift in the spectral exponent <em>α</em> from consciousness to unconsciousness. We investigated the impact of aperiodic EEG activity on parameters used for anaesthesia monitoring to test the hypothesis that aperiodic EEG activity carries information about the hypnotic component of general anaesthesia.</div></div><div><h3>Methods</h3><div>We used simulated noise with varying inverse power law exponents <em>α</em> and the aperiodic component of EEGs recorded during wakefulness (<em>n</em>=62) and maintenance of general anaesthesia (<em>n</em>=125) in a diverse sample of surgical patients receiving sevoflurane, desflurane, or propofol, extracted using the Fitting Oscillations and One-Over-F algorithm. Four spectral EEG parameters (beta ratio, spectral edge frequency 95, spectral entropy, and alpha-to-delta ratio) and two time-series parameters (approximate [ApEn] and permutation entropy [PeEn]) were calculated from the simulated signals and human EEG data. Performance in distinguishing between consciousness and unconsciousness was evaluated with AUC values.</div></div><div><h3>Results</h3><div>We observed an increase in the spectral exponent from consciousness to unconsciousness (AUC=0.98 (0.94–1)). The spectral parameters exhibited linear or nonlinear responses to changes in <em>α</em>. Using aperiodic EEG activity instead of the entire spectrum for spectral parameter calculation improved the separation between consciousness and unconsciousness for all parameters (AUC<sub><em>aperiodic</em></sub>=0.98 (0.94–1.00) <em>vs</em> AUC<sub><em>original</em></sub>=0.71 (0.62–0.79) to AUC<sub><em>original</em></sub>=0.95 (0.92–0.98)) up to the level of ApEn (AUC=0.96 (0.93–0.98)) and PeEn (AUC=0.94 (0.90–0.97)).</div></div><div><h3>Conclusions</h3><div>Aperiodic EEG activity could improve discrimination between consciousness and unconsciousness using spectral analyses.</div></div>\",\"PeriodicalId\":9250,\"journal\":{\"name\":\"British journal of anaesthesia\",\"volume\":\"134 2\",\"pages\":\"Pages 392-401\"},\"PeriodicalIF\":9.1000,\"publicationDate\":\"2025-02-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11775845/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"British journal of anaesthesia\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0007091224006482\",\"RegionNum\":1,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ANESTHESIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"British journal of anaesthesia","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0007091224006482","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ANESTHESIOLOGY","Score":null,"Total":0}

引用次数: 0

摘要

背景：非周期（非振荡）脑电图（EEG）活动可以通过其功率谱密度来表征，功率谱密度根据逆幂律衰减。先前的研究报告了光谱指数α从意识到无意识的转变。我们研究了非周期脑电图活动对麻醉监测参数的影响，以检验非周期脑电图活动携带全身麻醉催眠成分信息的假设。方法：我们在接受七氟醚、地氟醚或异丙酚治疗的不同手术患者样本中使用具有不同逆幂指数α的模拟噪声和在清醒（n=62）和维持全身麻醉（n=125）时记录的脑电图的非周期成分，使用拟合振荡和One-Over-F算法提取。从模拟信号和人脑电数据中计算出4个EEG频谱参数（β比、谱边频率95、谱熵和α - δ比）和2个时间序列参数（近似[ApEn]和置换熵[PeEn]）。用AUC值评价区分意识和无意识的表现。结果：我们观察到从意识到无意识的频谱指数增加（AUC=0.98(0.94-1)）。光谱参数对α的变化表现出线性或非线性的响应。使用非周期脑电图活动代替整个频谱进行频谱参数计算，将所有参数(AUCaperiodic=0.98 （0.94-1.00) vs AUCoriginal=0.71（0.62-0.79）至AUCoriginal=0.95(0.92-0.98)）的意识和无意识分离提高到ApEn （AUC=0.96(0.93-0.98)）和PeEn （AUC=0.94(0.90-0.97)）的水平。结论：利用频谱分析，非周期性脑电图活动可以提高意识和无意识的区分。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Aperiodic component of the electroencephalogram power spectrum reflects the hypnotic level of anaesthesia

Background

Aperiodic (nonoscillatory) electroencephalogram (EEG) activity can be characterised by its power spectral density, which decays according to an inverse power law. Previous studies reported a shift in the spectral exponent α from consciousness to unconsciousness. We investigated the impact of aperiodic EEG activity on parameters used for anaesthesia monitoring to test the hypothesis that aperiodic EEG activity carries information about the hypnotic component of general anaesthesia.

Methods

We used simulated noise with varying inverse power law exponents α and the aperiodic component of EEGs recorded during wakefulness (n=62) and maintenance of general anaesthesia (n=125) in a diverse sample of surgical patients receiving sevoflurane, desflurane, or propofol, extracted using the Fitting Oscillations and One-Over-F algorithm. Four spectral EEG parameters (beta ratio, spectral edge frequency 95, spectral entropy, and alpha-to-delta ratio) and two time-series parameters (approximate [ApEn] and permutation entropy [PeEn]) were calculated from the simulated signals and human EEG data. Performance in distinguishing between consciousness and unconsciousness was evaluated with AUC values.

Results

We observed an increase in the spectral exponent from consciousness to unconsciousness (AUC=0.98 (0.94–1)). The spectral parameters exhibited linear or nonlinear responses to changes in α. Using aperiodic EEG activity instead of the entire spectrum for spectral parameter calculation improved the separation between consciousness and unconsciousness for all parameters (AUC_aperiodic=0.98 (0.94–1.00) vs AUC_original=0.71 (0.62–0.79) to AUC_original=0.95 (0.92–0.98)) up to the level of ApEn (AUC=0.96 (0.93–0.98)) and PeEn (AUC=0.94 (0.90–0.97)).

Conclusions

Aperiodic EEG activity could improve discrimination between consciousness and unconsciousness using spectral analyses.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

British journal of anaesthesia 医学-麻醉学

CiteScore

13.50

自引率

7.10%

发文量

488

审稿时长

27 days

期刊介绍： The British Journal of Anaesthesia (BJA) is a prestigious publication that covers a wide range of topics in anaesthesia, critical care medicine, pain medicine, and perioperative medicine. It aims to disseminate high-impact original research, spanning fundamental, translational, and clinical sciences, as well as clinical practice, technology, education, and training. Additionally, the journal features review articles, notable case reports, correspondence, and special articles that appeal to a broader audience. The BJA is proudly associated with The Royal College of Anaesthetists, The College of Anaesthesiologists of Ireland, and The Hong Kong College of Anaesthesiologists. This partnership provides members of these esteemed institutions with access to not only the BJA but also its sister publication, BJA Education. It is essential to note that both journals maintain their editorial independence. Overall, the BJA offers a diverse and comprehensive platform for anaesthetists, critical care physicians, pain specialists, and perioperative medicine practitioners to contribute and stay updated with the latest advancements in their respective fields.