Highlights: ► Twelve entropy indices were systematically compared in monitoring depth of anesthesia and detecting burst suppression.► Renyi permutation entropy performed best in tracking EEG changes associated with different anesthesia states.► Approximate Entropy and Sample Entropy performed best in detecting burst suppression.Objective: Entropy algorithms have been widely used in analyzing EEG signals during anesthesia. However, a systematic comparison of these entropy algorithms in assessing anesthesia drugs' effect is lacking. In this study, we compare the capability of 12 entropy indices for monitoring depth of anesthesia (DoA) and detecting the burst suppression pattern (BSP), in anesthesia induced by GABAergic agents.Methods: Twelve indices were investigated, namely Response Entropy (RE) and State entropy (SE), three wavelet entropy (WE) measures [Shannon WE (SWE), Tsallis WE (TWE), and Renyi WE (RWE)], Hilbert-Huang spectral entropy (HHSE), approximate entropy (ApEn), sample entropy (SampEn), Fuzzy entropy, and three permutation entropy (PE) measures [Shannon PE (SPE), Tsallis PE (TPE) and Renyi PE (RPE)]. Two EEG data sets from sevoflurane-induced and isoflurane-induced anesthesia respectively were selected to assess the capability of each entropy index in DoA monitoring and BSP detection. To validate the effectiveness of these entropy algorithms, pharmacokinetic/pharmacodynamic (PK/PD) modeling and prediction probability (Pk) analysis were applied. The multifractal detrended fluctuation analysis (MDFA) as a non-entropy measure was compared.Results: All the entropy and MDFA indices could track the changes in EEG pattern during different anesthesia states. Three PE measures outperformed the other entropy indices, with less baseline variability, higher coefficient of determination (R2) and prediction probability, and RPE performed best; ApEn and SampEn discriminated BSP best. Additionally, these entropy measures showed an advantage in computation efficiency compared with MDFA.Conclusion: Each entropy index has its advantages and disadvantages in estimating DoA. Overall, it is suggested that the RPE index was a superior measure. Investigating the advantages and disadvantages of these entropy indices could help improve current clinical indices for monitoring DoA.

The aim of this report was to confirm the methodology of bispectral analysis of electroencephalogram. In developing a software for real-time bispectral analysis, we encountered several practical problems in bispectrum calculation. We settled those and concluded that 3 min of monitoring are required to obtain reliable and reproducible bicoherence values.

The relationship between bispectral index (BIS) and electroencephalographic parameters was evaluated during nitrous oxide/isoflurane anesthesia. At surgical levels of anesthesia, BIS and the relative synchrony of fast and slow wave (a parameter derived from bispectral analysis) or burst-compensated spectral edge frequency 95% (a parameter derived from power spectral analysis) are well correlated.

The use of EEG monitors to assess the level of hypnosis during anaesthesia has become widespread. Anaesthetists, however, do not usually observe the raw EEG data: they generally pay attention only to the Bispectral Index (BIS™) and other indices calculated by EEG monitors. This abstracted information only partially characterizes EEG features. To properly appreciate the availability and reliability of EEG-derived indices, it is necessary to understand how raw EEG changes during anaesthesia. With hemi-frontal lead EEGs obtained under volatile anaesthesia or propofol anaesthesia, the dominant EEG frequency decreases and the amplitude increases with increasing concentrations of anaesthetic. Looking more closely, the EEG changes are more complicated. At surgical concentrations of anaesthesia, spindle waves (alpha range) become dominant. At deeper levels, this activity decreases, and theta and delta waves predominate. At even deeper levels, EEG waveform changes into a burst and suppression pattern, and finally becomes flat. EEG waveforms vary in the presence of noxious stimuli (surgical skin incision), which is not always reflected in BIS™, or other processed EEG indices. Spindle waves are adequately sensitive, however, to noxious stimuli: under surgical anaesthesia they disappear when noxious stimuli are applied, and reappear when adequate analgesia is obtained. To prevent awareness during anaesthesia, I speculate that the most effective strategy is to administer anaesthetic agents in such a way as to maintain anaesthesia at a level where spindle waves predominate.

Noxious stimuli decreased the peak heights of electroencephalographic bicoherence, an effect that was counteracted by fentanyl analgesia.