Zhenhu Liang

Multiscale permutation entropy analysis of EEG recordings during sevoflurane anesthesia

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.

Multiscale rescaled range analysis of EEG recordings in sevoflurane anesthesia

OBJECTIVE The Hurst exponent (HE) is a nonlinear method measuring the smoothness of a fractal time series. In this study we applied the HE index, extracted from electroencephalographic (EEG) recordings, as a measure of anesthetic drug effects on brain activity. METHODS In 19 adult patients undergoing sevoflurane general anesthesia, we calculated the HE of the raw EEG; comparing the maximal overlap discrete wavelet transform (MODWT) with the traditional rescaled range (R/S) analysis techniques, and with a commercial index of depth of anesthesia - the response entropy (RE). We analyzed each wavelet-decomposed sub-band as well as the combined low frequency bands (HEOLFBs). The methods were compared in regard to pharmacokinetic/pharmacodynamic (PK/PD) modeling, and prediction probability. RESULTS All the low frequency band HE indices decreased when anesthesia deepened. However the HEOLFB was the best index because: it was less sensitive to artifacts, most closely tracked the exact point of loss of consciousness, showed a better prediction probability in separating the awake and unconscious states, and tracked sevoflurane concentration better - as estimated by the PK/PD models. CONCLUSIONS The HE is a useful measure for estimating the depth of anesthesia. It was noted that HEOLFB showed the best performance for tracking drug effect. SIGNIFICANCE The HEOLFB could be used as an index for accurately estimating the effect of anesthesia on brain activity.

Spinal cord stimulation modulates frontal delta and gamma in patients of minimally consciousness state

Spinal cord stimulation (SCS) has been suggested as a therapeutic technique for treating patients with disorder of consciousness (DOC). Although studies have reported its benefits for patients, the underlying pathophysiological mechanisms remain unclear. The aim of this study was to measure the effects of SCS on the EEG of patients in a minimally conscious state (MCS), which would allow us to explore the possible workings underpinning of the approach. Resting state EEG was recorded before and immediately after SCS, using various frequencies (5Hz, 20Hz, 50Hz, 70Hz and 100Hz), for 11 patients in MCS. Relative power, coherence, S-estimator and bicoherence were calculated to assess the EEG changes. Five frequency bands (delta, theta, alpha, beta and gamma) and three regions (frontal, central and posterior) were divided in the calculation. The main findings of this study were that: (1) significantly altered relative power and synchronisation was found in delta and gamma bands after one SCS stimulation using 5Hz, 70Hz or 100Hz; (2) bicoherence showed that coupling within delta was significantly decreased after stimulation using 70Hz, while reduction of coupling between delta and gamma was found when using 5Hz and 100Hz. However, SCS of 20Hz, 50Hz and sham stimulation did not induce changes in any frequency band at any region. This study showed EEG evidence that SCS can modulate the brain function of MCS patients, speculatively by activating the formation-thalamus-cortex network.

Detection of Burst Suppression Patterns in EEG Using Recurrence Rate

Burst suppression is a unique electroencephalogram (EEG) pattern commonly seen in cases of severely reduced brain activity such as overdose of general anesthesia. It is important to detect burst suppression reliably during the administration of anesthetic or sedative agents, especially for cerebral-protective treatments in various neurosurgical diseases. This study investigates recurrent plot (RP) analysis for the detection of the burst suppression pattern (BSP) in EEG. The RP analysis is applied to EEG data containing BSPs collected from 14 patients. Firstly we obtain the best selection of parameters for RP analysis. Then, the recurrence rate (RR), determinism (DET), and entropy (ENTR) are calculated. Then RR was selected as the best BSP index one-way analysis of variance (ANOVA) and multiple comparison tests. Finally, the performance of RR analysis is compared with spectral analysis, bispectral analysis, approximate entropy, and the nonlinear energy operator (NLEO). ANOVA and multiple comparison tests showed that the RR could detect BSP and that it was superior to other measures with the highest sensitivity of suppression detection (96.49%, P = 0.03). Tracking BSP patterns is essential for clinical monitoring in critically ill and anesthetized patients. The purposed RR may provide an effective burst suppression detector for developing new patient monitoring systems.

A permutation Lempel-Ziv complexity measure for EEG analysis

Abstract Objective In this study we develop a new complexity measure of time series by combining ordinal patterns and Lempel-Ziv complexity (LZC) for quantifying the dynamical changes of EEG. Methods A neural mass model (NMM) was used to simulate EEG data and test the performance of the permutation Lempel-Ziv complexity (PLZC) in tracking the dynamical changes of signals against different white noise levels. Then, the PLZC was applied to real EEG data to investigate whether it was able to detect the different states of anesthesia and epileptic seizures. The Z-score model, two-way ANOVA and t -test were used to estimate the significance of the results. Results PLZC could successfully track the dynamical changes of EEG series generated by the NMM. Compared with the other four classical LZC based methods, the PLZC was most robust against white noise. In real data analysis, PLZC was effective in differentiating the different anesthesia states and sensitive in detecting epileptic seizures. Conclusions PLZC is simple, robust and effective for quantifying the dynamical changes of EEG. Significance We suggest that PLZC is a potential nonlinear method for characterizing the changes in EEG signal.

Permutation auto-mutual information of electroencephalogram in anesthesia.

OBJECTIVE The dynamic change of brain activity in anesthesia is an interesting topic for clinical doctors and drug designers. To explore the dynamical features of brain activity in anesthesia, a permutation auto-mutual information (PAMI) method is proposed to measure the information coupling of electroencephalogram (EEG) time series obtained in anesthesia. APPROACH The PAMI is developed and applied on EEG data collected from 19 patients under sevoflurane anesthesia. The results are compared with the traditional auto-mutual information (AMI), SynchFastSlow (SFS, derived from the BIS index), permutation entropy (PE), composite PE (CPE), response entropy (RE) and state entropy (SE). Performance of all indices is assessed by pharmacokinetic/pharmacodynamic (PK/PD) modeling and prediction probability. MAIN RESULTS The PK/PD modeling and prediction probability analysis show that the PAMI index correlates closely with the anesthetic effect. The coefficient of determination R(2) between PAMI values and the sevoflurane effect site concentrations, and the prediction probability Pk are higher in comparison with other indices. The information coupling in EEG series can be applied to indicate the effect of the anesthetic drug sevoflurane on the brain activity as well as other indices. The PAMI of the EEG signals is suggested as a new index to track drug concentration change. SIGNIFICANCE The PAMI is a useful index for analyzing the EEG dynamics during general anesthesia.

A Comparison of Multiscale Permutation Entropy Measures in On-Line Depth of Anesthesia Monitoring.

Objective Multiscale permutation entropy (MSPE) is becoming an interesting tool to explore neurophysiological mechanisms in recent years. In this study, six MSPE measures were proposed for on-line depth of anesthesia (DoA) monitoring to quantify the anesthetic effect on the real-time EEG recordings. The performance of these measures in describing the transient characters of simulated neural populations and clinical anesthesia EEG were evaluated and compared. Methods Six MSPE algorithms—derived from Shannon permutation entropy (SPE), Renyi permutation entropy (RPE) and Tsallis permutation entropy (TPE) combined with the decomposition procedures of coarse-graining (CG) method and moving average (MA) analysis—were studied. A thalamo-cortical neural mass model (TCNMM) was used to generate noise-free EEG under anesthesia to quantitatively assess the robustness of each MSPE measure against noise. Then, the clinical anesthesia EEG recordings from 20 patients were analyzed with these measures. To validate their effectiveness, the ability of six measures were compared in terms of tracking the dynamical changes in EEG data and the performance in state discrimination. The Pearson correlation coefficient (R) was used to assess the relationship among MSPE measures. Results CG-based MSPEs failed in on-line DoA monitoring at multiscale analysis. In on-line EEG analysis, the MA-based MSPE measures at 5 decomposed scales could track the transient changes of EEG recordings and statistically distinguish the awake state, unconsciousness and recovery of consciousness (RoC) state significantly. Compared to single-scale SPE and RPE, MSPEs had better anti-noise ability and MA-RPE at scale 5 performed best in this aspect. MA-TPE outperformed other measures with faster tracking speed of the loss of unconsciousness. Conclusions MA-based multiscale permutation entropies have the potential for on-line anesthesia EEG analysis with its simple computation and sensitivity to drug effect changes. CG-based multiscale permutation entropies may fail to describe the characteristics of EEG at high decomposition scales.

A comparison of different synchronization measures in electroencephalogram during propofol anesthesia

Abstract Electroencephalogram (EEG) synchronization is becoming an essential tool to describe neurophysiological mechanisms of communication between brain regions under general anesthesia. Different synchronization measures have their own properties to reflect the changes of EEG activities during different anesthetic states. However, the performance characteristics and the relations of different synchronization measures in evaluating synchronization changes during propofol-induced anesthesia are not fully elucidated. Two-channel EEG data from seven volunteers who had undergone a brief standardized propofol anesthesia were then adopted to calculate eight synchronization indexes. We computed the prediction probability (PK) of synchronization indexes with Bispectral Index (BIS) and propofol effect-site concentration (Ceff) to quantify the ability of the indexes to predict BIS and Ceff. Also, box plots and coefficient of variation were used to reflect the different synchronization changes and their robustness to noise in awake, unconscious and recovery states, and the Pearson correlation coefficient (R) was used for assessing the relationship among synchronization measures, BIS and Ceff. Permutation cross mutual information (PCMI) and determinism (DET) could predict BIS and follow Ceff better than nonlinear interdependence (NI), mutual information based on kernel estimation (KerMI) and cross correlation. Wavelet transform coherence (WTC) in α and β frequency bands followed BIS and Ceff better than that in other frequency bands. There was a significant decrease in unconscious state and a significant increase in recovery state for PCMI and NI, while the trends were opposite for KerMI, DET and WTC. Phase synchronization based on phase locking value (PSPLV) in δ, θ, α and γ1 frequency bands dropped significantly in unconscious state, whereas it had no significant synchronization in recovery state. Moreover, PCMI, NI, DET correlated closely with each other and they had a better robustness to noise and higher correlation with BIS and Ceff than other synchronization indexes. Propofol caused EEG synchronization changes during the anesthetic period. Different synchronization measures had individual properties in evaluating synchronization changes in different anesthetic states, which might be related to various forms of neural activities and neurophysiological mechanisms under general anesthesia.

Permutation Lempel–Ziv complexity measure of electroencephalogram in GABAergic anaesthetics

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.

Comparison of Permutation Entropy Index and Bispectral Index for Monitoring Effects of Anesthetic Drugs to Brain Activity

This paper focuses on the comparison of the permutation entropy index and the bispectral index for the estimation of depth of anesthesia. First, we improve the permutation entropy index proposed in [1] with an outlier detection method. Then, the correlation between the permutation entropy index and the bispectral index of EEG series is calculated with seven patients’ EEG recordings. A critical anesthesia line is quantified to ensure that the permutation entropy index could obtain a similar result with the bispectral index. The significant correlation and high computational efficacy make sure that the permutation entropy index could replace the bispectral index for estimation of depth of anesthesia in the future.