A comparison of different synchronization measures in electroencephalogram during propofol anesthesia

被引:0
作者
Zhenhu Liang
Ye Ren
Jiaqing Yan
Duan Li
Logan J. Voss
Jamie W. Sleigh
Xiaoli Li
机构
[1] Yanshan University,Institute of Electric Engineering
[2] Yanshan University,Key Laboratory of Industrial Computer Control Engineering of Hebei Province
[3] Yanshan University,Institute of Information Science and Engineering
[4] Waikato Hospital,Department of Anaesthesia
[5] University of Jyvaskyla,Department of Mathematical Information Technology
来源
Journal of Clinical Monitoring and Computing | 2016年 / 30卷
关键词
Electroencephalogram; Loss of consciousness; Neurophysiological mechanisms; Propofol anesthesia; Synchronization measures;
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摘要
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.
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页码:451 / 466
页数:15
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共 235 条
[1]  
Lewis LD(2012)Rapid fragmentation of neuronal networks at the onset of propofol-induced unconsciousness Proc Natl Acad Sci 109 E3377-E3386
[2]  
Weiner VS(2007)Monitoring consciousness: the current status of EEG-based depth of anaesthesia monitors Best Pract Res Clin Anaesthesiol 21 313-325
[3]  
Mukamel EA(2011)Functional connectivity in the brain: effects of anesthesia Neuroscientist 17 94-106
[4]  
Donoghue JA(2009)Propofol induction reduces the capacity for neural information integration: implications for the mechanism of consciousness and general anesthesia Conscious Cogn 18 56-64
[5]  
Eskandar EN(2009)The directionality and functional organization of frontoparietal connectivity during consciousness and anesthesia in humans Conscious Cogn 18 1069-1078
[6]  
Madsen JR(2005)Nonlinear multivariate analysis of neurophysiological signals Prog Neurobiol 77 1-37
[7]  
Anderson WS(2004)“Dynamic” connectivity in neural systems Neuroinformatics 2 205-224
[8]  
Hochberg LR(2005)Nonlinear dynamical analysis of EEG and MEG: review of an emerging field Clin Neurophysiol 116 2266-2301
[9]  
Cash SS(2007)Increased amygdala and decreased dorsolateral prefrontal BOLD responses in unipolar depression: related and independent features Biol Psychiatry 61 198-209
[10]  
Brown EN(2008)Electrophysiological correlates of the brain’s intrinsic large-scale functional architecture Proc Natl Acad Sci USA 105 16039-16044