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A comparison of different synchronization measures in electroencephalogram during propofol anesthesia

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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 (P K ) of synchronization indexes with Bispectral Index (BIS) and propofol effect-site concentration (C eff ) to quantify the ability of the indexes to predict BIS and C eff . 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 C eff . Permutation cross mutual information (PCMI) and determinism (DET) could predict BIS and follow C eff 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 C eff 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 C eff 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.

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Acknowledgments

This research was supported by National Natural Science Foundation of China (61304247, 61203210, 61273063), China Postdoctoral Science Foundation (2014M551051), Applied basic research project in Hebei province (12966120D) and Natural Science Foundation of Hebei Province of China (F2014203127).

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    Authors and Affiliations

    1. Institute of Electric Engineering, Yanshan University, Qinhuangdao, 066004, China

      Zhenhu Liang, Ye Ren, Jiaqing Yan & Xiaoli Li

    2. Key Laboratory of Industrial Computer Control Engineering of Hebei Province, Yanshan University, Qinhuangdao, 066004, China

      Zhenhu Liang & Xiaoli Li

    3. Institute of Information Science and Engineering, Yanshan University, Qinhuangdao, 066004, China

      Duan Li

    4. Department of Anaesthesia, Waikato Hospital, Hamilton, New Zealand

      Logan J. Voss & Jamie W. Sleigh

    5. Department of Mathematical Information Technology, University of Jyvaskyla, Jyvaskyla, Finland

      Ye Ren

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    1. Zhenhu Liang
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    Zhenhu Liang and Ye Ren have equally contributed to this work.

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    Fig. S1

    PSPLV values of five frequency bands (δ, θ, α, β and γ1) of different epoch length T e in awake state (red), unconscious state (green) and recovery state (blue) (TIFF 157 kb)

    Fig. S2

    PSSE values of five frequency bands (δ, θ, α, β and γ1) of different epoch length T e in awake state (red), unconscious state (green) and recovery state (blue) (TIFF 168 kb)

    Fig. S3

    NI values of different parameters embedding dimension m, time lag τ and the number of nearest neighbors k in awake state (red), unconscious state (green) and recovery state (blue) (TIFF 104 kb)

    Fig. S4

    DET values of different parameters embedding dimension m, time lag τ and threshold of diagonal length l min in awake state (red), unconscious state (green) and recovery state (blue) (TIFF 99 kb)

    Appendices

    Appendix 1

    In order to evaluate the synchronization changes in different anesthetic states efficiently, we discussed the parameter selections of PS, NI and DET. We calculated these synchronization indexes under different parameters of all subjects and chose three datasets from each synchronization index in awake, unconscious and recovery states which were according to the time points of each subjects. The values of synchronization indexes under different parameters were shown in Fig. S1-S4. All values were given by median (Q1, Q3).

    1.1 PS

    Fig. S1 and Fig. S2 showed the PSPLV (δ, θ, α, β and γ1) and PSSE (δ, θ, α, β and γ1) values at different epoch length T e in awake state (red), unconscious state (green) and recovery state (blue) of all subjects. It can be seen from Fig. S1 that the PSPLV values of all frequency bands decreased with increasing T e . The difference between awake and unconscious states of PSPLV (δ) were larger than PSPLV in other frequency bands, which was also could be seen from Fig. 4f. By contrast, PSSE had some fluctuation at different T e (Fig. S2). T e  = 20 was used in our study.

    1.2 NI

    Fig. S3A showed the NI values with time lag τ = 1, nearest neighbors k = 20 in different embedding dimension m in awake state (red), unconscious state (green) and recovery state (blue) of all subjects. The NI values with τ = 2, k = 20 in different m were shown in Fig. S3B. As can be seen from these two figures, NI increased monotonically with increasing m and the difference of NI values between awake, unconscious and recovery states became wider with increasing m. Therefore, m = 5 was selected in terms of calculation complexity. Figure S3C showed NI values with m = 5, k = 20 in different τ. The NI difference between awake and unconscious states became smaller with increasing τ, so we chose τ = 1. The NI values with m = 5, τ = 1 in different nearest neighbors k were shown in Fig. S3D and we selected k = 20.

    1.3 DET

    Figures S4A, S4B and S4C showed DET values with embedding dimension m = 3, m = 4 and m = 5 respectively in threshold of diagonal length l min  = 2 in different time lag τ in awake state (red), unconscious state (green) and recovery state (blue) of all subjects. m = 3, τ = 2 were selected because of the great DET difference between awake and unconscious states. DET values with m = 3, τ = 2 in different l min were shown in Fig. S4D and l min  = 2 was selected.

    Appendix 2

    We used the MATLAB programs lagged.m to compute COR, which can be downloaded from the Functional Connectivity Toolbox (https://sites.google.com/site/functionalconnectivitytoolbox/). The MATLAB programs of PSSE and PSCP (nbt_n_m_detection.m) can be downloaded from the Neurophysiological Biomarker Toolbox (https://www.nbtwiki.net/doku.php?id=tutorial:phase_locking_value#.VWm2zmgyGlB). The MATLAB programs of KerMI (FastPairMI.m) can be downloaded from http://pengqiu.gatech.edu/software/FastPairMI/index.htm. The MATLAB program of NI (synchro.m) can be downloaded from https://vis.caltech.edu/~rodri/software.htm. The MATLAB programs of PCMI, PSPLV, DET and WTC are available by contacting the corresponding author.

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    Liang, Z., Ren, Y., Yan, J. et al. A comparison of different synchronization measures in electroencephalogram during propofol anesthesia. J Clin Monit Comput 30, 451–466 (2016). https://doi.org/10.1007/s10877-015-9738-z

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