Widmann Sandra, Ostertag Julian, Zinn Sebastian, Pilge Stefanie, García Paul S, Kratzer Stephan, Schneider Gerhard, Kreuzer Matthias
Department of Anesthesiology and Intensive Care, School of Medicine and Health, Technical University of Munich, Munich, Germany.
Klinik für Anästhesiologie, Intensivmedizin und Schmerztherapie, Goethe-Universität Frankfurt, Frankfurt am Main, Germany; Department of Anesthesiology, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, USA.
Br J Anaesth. 2025 Feb;134(2):392-401. doi: 10.1016/j.bja.2024.09.027. Epub 2024 Nov 28.
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.
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.
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=0.98 (0.94-1.00) vs AUC=0.71 (0.62-0.79) to AUC=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)).
Aperiodic EEG activity could improve discrimination between consciousness and unconsciousness using spectral analyses.
非周期性（非振荡性）脑电图（EEG）活动可通过其功率谱密度来表征，该功率谱密度根据反幂律衰减。先前的研究报道，频谱指数α从清醒状态到无意识状态会发生变化。我们研究了非周期性EEG活动对用于麻醉监测的参数的影响，以检验非周期性EEG活动携带有关全身麻醉催眠成分信息的假设。
我们使用了具有不同反幂律指数α的模拟噪声，以及在接受七氟醚、地氟醚或丙泊酚的不同外科手术患者样本中，清醒状态（n = 62）和全身麻醉维持期间记录的EEG的非周期性成分，使用拟合振荡和一除以算法进行提取。从模拟信号和人类EEG数据中计算出四个频谱EEG参数（β比率、频谱边缘频率95、频谱熵和α与δ比率）以及两个时间序列参数（近似熵[ApEn]和排列熵[PeEn]）。使用AUC值评估区分清醒和无意识状态的性能。
我们观察到从清醒到无意识状态频谱指数增加（AUC = 0.98（0.94 - 1））。频谱参数对α的变化表现出线性或非线性响应。使用非周期性EEG活动而非整个频谱进行频谱参数计算，可改善所有参数在清醒和无意识状态之间的区分度（AUC = 0.98（0.94 - 1.00），而之前为AUC = 0.71（0.62 - 0.79）至AUC = 0.95（0.92 - 0.98）），直至达到ApEn（AUC = 0.96（0.93 - 0.98））和PeEn（AUC = 0.94（0.90 - 0.97））的水平。
非周期性EEG活动可通过频谱分析改善对清醒和无意识状态的区分度。
