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

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 (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.