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.

Electroencephalogram (EEG) monitoring of the effect of anesthetic drugs on the central nervous system has long been used in anesthesia research. Several methods based on nonlinear dynamics, such as permutation entropy (PE), have been proposed to analyze EEG series during anesthesia. However, these measures are still single-scale based and may not completely describe the dynamical characteristics of complex EEG series. In this paper, a novel measure combining multiscale PE information, called CMSPE (composite multi-scale permutation entropy), was proposed for quantifying the anesthetic drug effect on EEG recordings during sevoflurane anesthesia. Three sets of simulated EEG series during awake, light and deep anesthesia were used to select the parameters for the multiscale PE analysis: embedding dimension m, lag tau and scales to be integrated into the CMSPE index. Then, the CMSPE index and raw single-scale PE index were applied to EEG recordings from 18 patients who received sevoflurane anesthesia. Pharmacokinetic/pharmacodynamic (PKPD) modeling was used to relate the measured EEG indices and the anesthetic drug concentration. Prediction probability (P(k)) statistics and correlation analysis with the response entropy (RE) index, derived from the spectral entropy (M-entropy module; GE Healthcare, Helsinki, Finland), were investigated to evaluate the effectiveness of the new proposed measure. It was found that raw single-scale PE was blind to subtle transitions between light and deep anesthesia, while the CMSPE index tracked these changes accurately. Around the time of loss of consciousness, CMSPE responded significantly more rapidly than the raw PE, with the absolute slopes of linearly fitted response versus time plots of 0.12 (0.09-0.15) and 0.10 (0.06-0.13), respectively. The prediction probability P(k) of 0.86 (0.85-0.88) and 0.85 (0.80-0.86) for CMSPE and raw PE indicated that the CMSPE index correlated well with the underlying anesthetic effect. The correlation coefficient for the comparison between the CMSPE index and RE index of 0.84 (0.80-0.88) was significantly higher than the raw PE index of 0.75 (0.66-0.84). The results show that the CMSPE outperforms the raw single-scale PE in reflecting the sevoflurane drug effect on the central nervous system.

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Monitoring the brain state in anaesthesia is crucial for clinical doctors. In this study, we propose a novel nonlinear method, the permutation Lempel-Ziv complexity (PLZC) index, which describes the complexity in the electroencephalographic (EEG) signal to quantify the effect of GABAergic anaesthetics on brain activities.We applied the PLZC to two EEG data sets that were recorded under sevoflurane and propofol anaesthesia. The results are compared with traditional mean value-based Lempel-Ziv complexity (LZC), permutation entropy (PE), composite PE index (CPEI), response entropy (RE), state entropy (SE) and bispectral index (BIS) or SynchFastSlow (SFS, derived from BIS). Pharmacokinetic/pharmacodynamic (PK/PD) modelling and prediction probability (Pk) were used to assess the performance of the proposed method for tracking GABAergic anaesthetic concentrations.We found that PLZC correlates closely with the anaesthetic drug effect. When applied in sevoflurane anaesthesia, the coefficient of determination R2 between the PLZC values and the sevoflurane effect site concentrations was (0.90 ± 0.07), mean  ±  standard deviation), and the prediction probability Pk was (0.85 ± 0.04). These values were higher than those for the other indices. While in propofol anaesthesia, the value of R2 between PLZC and the effect site concentrations was (0.89 +/- 0.07), and the Pk was (0.86 +/- 0.28), which were close to those for CPEI but better than those for the others.PLZC based on electroencephalogram signals can be used as a new index to characterize the depth of anaesthesia. This index outperformed LZC, PE, CPEI, RE, SE, and BIS or SFS in tracking drug concentration changes during GABAergic anaesthetics.PLZC is a potentially superior method for applications in intra-operative monitoring.

Background The systemic low‐frequency oscillation (sLFO) functional (f)MRI signals extracted from the internal carotid artery (ICA) and the superior sagittal sinus (SSS) are found to have valuable physiological information. Purpose 1) To further develop and validate a method utilizing these signals to measure the delay times from the ICAs and the SSS. 2) To establish the delay time as an effective perfusion biomarker that associates with cerebral circulation time (CCT). 3) To explore within subject variations, and the effects of gender and age on the delay times. Study Type Prospective. Subjects In all, 100 healthy adults (Human Connectome Project [HCP], age range 22–36 years, 54 females and 46 males), 56 healthy children (Adolescent Brain Cognitive Development project) were included. Field Strength/Sequence Echo planar imaging (EPI) sequence at 3T. Assessment The sLFO fMRI signals from the ICAs and the SSSs were extracted from the resting state fMRI data. The maximum cross‐correlation coefficients and their corresponding delay times were calculated. The gender and age differences of delay times were assessed statistically. Statistical Tests T‐tests were conducted to measure the gender differences. The Kruskal–Wallis test was used to detect age differences. Results Consistent and robust results were found from 80% of the 400 HCP scans included. Negative correlations (–0.67) between the ICA and the SSS signals were found with the ICA signal leading the SSS signal by ∼5 sec. Within subject variation was 2.23 sec at the 5% significance level. The delay times were not significantly different between genders (P = 0.9846, P = 0.2288 for the left and right ICA, respectively). Significantly shorter delay times (4.3 sec) were found in the children than in the adults (P < 0.01). Data Conclusion We have shown that meaningful perfusion information (ie, CCT) can be derived from the sLFO fMRI signals of the large blood vessels. Level of Evidence: 1 Technical Efficacy Stage: 1 J. Magn. Reson. Imaging 2019;50:1504–1513.

This study demonstrated that, in a small sample, multivariate pattern analysis can effectively identify ADHD children from healthy controls based on fNIRS signals, which argues for the potential utility of fNIRS in future assessments.