Sandra Widmann, Julian Ostertag, Sebastian Zinn, Stefanie Pilge, Paul S García, Stephan Kratzer, Gerhard Schneider, Matthias Kreuzer
{"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":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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<sub>aperiodic</sub>=0.98 (0.94-1.00) vs AUC<sub>original</sub>=0.71 (0.62-0.79) to AUC<sub>original</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)).</p><p><strong>Conclusions: </strong>Aperiodic EEG activity could improve discrimination between consciousness and unconsciousness using spectral analyses.</p>","PeriodicalId":9250,"journal":{"name":"British journal of anaesthesia","volume":" ","pages":""},"PeriodicalIF":9.1000,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"British journal of anaesthesia","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1016/j.bja.2024.09.027","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
Abstract
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 (AUCaperiodic=0.98 (0.94-1.00) vs AUCoriginal=0.71 (0.62-0.79) to AUCoriginal=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.
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.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
