Cognitive Neurodynamics

, Volume 13, Issue 6, pp 531–540 | Cite as

Frontal–temporal functional connectivity of EEG signal by standardized permutation mutual information during anesthesia

  • Fahimeh Afshani
  • Ahmad ShalbafEmail author
  • Reza Shalbaf
  • Jamie Sleigh
Research Article


Quantifying brain dynamics during anesthesia is an important challenge for understanding the neurophysiological mechanisms of anesthetic drug effect. Several single channel Electroencephalogram (EEG) indices have been proposed for monitoring anesthetic drug effect. The most commonly used single channel commercial index is the Bispectral index (BIS). However, this monitor has shown some drawbacks. In this study, a nonlinear functional connectivity measure named Standardized Permutation Mutual Information (SPMI) is proposed to describe communication between two-channel EEG signals at frontal and temporal brain regions during a controlled propofol-induced anesthesia and recovery design from eight subjects. The SPMI index has higher correlation with estimated propofol effect-site concentration and has better ability to distinguish three anesthetic states of patient than the other functional connectivity indexes (cross-correlation, coherence, phase analysis) and also the BIS index. Moreover, the SPMI index has a faster reaction to the effect of drug concentration, less variability at the consciousness state and better robustness to noise than BIS.


Electroencephalogram Depth of anesthesia Mutual information Bispectral index 



  1. Abásolo D, Escudero J, Hornero R, Gómez C, Espino P (2008) Approximate entropy and auto mutual information analysis of the electroencephalogram in Alzheimer’s disease patients. Med Biol Eng Comput 46(10):1019–1028PubMedGoogle Scholar
  2. Akeju O, Westover MB, Pavone KJ, Sampson AL, Hartnack KE, Brown EN, Purdon PL (2014) Effects of sevoflurane and propofol on frontal electroencephalogram power and coherence. Anesthesiology 121(5):990–998PubMedPubMedCentralGoogle Scholar
  3. Alkire MT, Hudetz AG, Tononi G (2008) Consciousness and anesthesia. Science 322:876–880PubMedPubMedCentralGoogle Scholar
  4. Bandt C, Pompe B (2002) Permutation entropy: a natural complexity measure for time series. Phys Rev Lett 88(17):174102PubMedGoogle Scholar
  5. Breakspear M (2004) Dynamic connectivity in neural systems. Neuroinformatics 2(2):205–224Google Scholar
  6. Drexler B, Roether CL, Jurd R, Rudolph U, Antkowiak B (2005) Opposing actions of etomidate on cortical theta oscillations are mediated by different γ amino butyric acid type A receptor subtypes. Anesthesiology 102:346–352PubMedGoogle Scholar
  7. Ferrarelli F, Massimini M, Sarasso S et al (2010) Breakdown in cortical effective connectivity during midazolam-induced loss of consciousness. Proc Natl Acad Sci USA 107:2681–2686PubMedGoogle Scholar
  8. Gifani P, Rabiee HR, Hashemi MH, Zadeh MS, Taslimi P, Ghanbari M (2007) Optimal fractal-scaling analysis of human EEG dynamic for depth of anesthesia quantification. J Frankl Inst 344:212–229Google Scholar
  9. Gugino LD, Chabot RJ, Prichep LS, John ER, Formanek V, Aglio LS (2001) Quantitative EEG changes associated with loss and return of consciousness in healthy adult volunteers anaesthetized with propofol or sevoflurane. Br J Anaesth 87:421–428PubMedGoogle Scholar
  10. Hagihira S, Takashina M, Mori T, Mashimo T, Yoshiya I (2001) Practical issues in bispectral analysis of electroencephalographic signals. Anesth Analg 93:966–970PubMedGoogle Scholar
  11. Hall CW Jr, Sarkar A (2011) Mutual information in natural position order of electroencephalogram is significantly increased at seizure onset. Med Biol Eng Comput 49(2):133–141PubMedGoogle Scholar
  12. Hasenstaub A, Shu Y, Haider B et al (2005) Inhibitory postsynaptic potentials carry synchronized frequency information in active cortical networks. Neuron 47:423–435PubMedGoogle Scholar
  13. Hayashi K, Mukai N, Sawa T (2014) Simultaneous bicoherence analysis of occipital and frontal electroencephalograms in awake and anesthetized subjects. Clin Neurophysiol 125(1):194–201PubMedGoogle Scholar
  14. Hejazi M, Nasrabadi AM (2019) Prediction of epilepsy seizure from multi-channel electroencephalogram by effective connectivity analysis using Granger causality and directed transfer function methods. Cogn Neurodyn. CrossRefPubMedGoogle Scholar
  15. Hou D, Wang C, Chen Y, Wang W, Du J (2017) Long-range temporal correlations of broadband EEG oscillations for depressed subjects following different hemispheric cerebral infarction. Cogn Neurodyn 11:529–538PubMedPubMedCentralGoogle Scholar
  16. Hudetz AG (2002) Effect of volatile anesthetics on interhemispheric EEG crossapproximate entropy. Brain Res 954:123–131PubMedGoogle Scholar
  17. Imas OA, Ropella KM, Wood JD, Hudetz AG (2006) Isoflurane disrupts anterio-posterior phase synchronization of flash-induced field potentials in the rat. Neurosci Lett 402:216–221PubMedGoogle Scholar
  18. Johansen JW, Sebel PS (2000) Development and clinical application of electroencephalographic bispectrum monitoring. Anesthesiology 93:1336–1344PubMedGoogle Scholar
  19. John ER, Prichep LS, Kox W et al (2001) Invariant reversible QEEG effects of anesthetics. Conscious Cogn 10:165–183PubMedGoogle Scholar
  20. Ku SW, Lee U, Noh GJ, Jun IG, Mashour GA (2011) Preferential inhibition of frontal-to-parietal feedback connectivity is a neurophysiologic correlate of general anesthesia in surgical patients. PLoS ONE 6:10Google Scholar
  21. Kuhlmann L et al (2017) Tracking electroencephalographic changes using distributions of linear models: application to propofol-based depth of anesthesia monitoring. IEEE Trans Biomed Eng 64:870–881PubMedGoogle Scholar
  22. Lachaux JP, Rodriguez E, Martinerie J, Varela FJ (1999) Measuring phase synchrony in brain signals. Hum Brain Mapp 8(4):194–208PubMedPubMedCentralGoogle Scholar
  23. Langen M, Schnack HG, Nederveen H, Bos D, Lahuis BE, de Jonge MV, van Engeland H, Durston S (2009) Changes in the developmental trajectories of striatum in autism. Biol Psychiatry 66(4):327–333PubMedGoogle Scholar
  24. Lee U, Kim S, Noh G-J, Choi B-M, Hwang E, Mashour GA (2009a) The directionality and functional organization of frontoparietal connectivity during consciousness and anesthesia in humans. Conscious Cogn 18(4):1069–1078PubMedGoogle Scholar
  25. Lee U, Mashour GA, Kim S, Noh G-J, Choi B-M (2009b) Propofol induction reduces the capacity for neural information integration: implications for the mechanism of consciousness and general anesthesia. Conscious Cogn 18(1):56–64PubMedGoogle Scholar
  26. Li T, Wen P (2017) Depth of anaesthesia assessment using interval second-order difference plot and permutation entropy techniques. IET Signal Proc 11:221–227Google Scholar
  27. Li D, Voss LJ, Sleigh JW, Li X (2013) Effects of volatile anesthetic agents on cerebral cortical synchronization in sheep. Anesthesiology 119(1):81–88PubMedGoogle Scholar
  28. Li D, Hambrechtwiedbusch VS, Mashour GA (2017a) Accelerated recovery of consciousness after general anesthesia is associated with increased functional brain connectivity in the high-gamma bandwidth. Front Syst Neurosci 11:16PubMedPubMedCentralGoogle Scholar
  29. Li X, Wang F, Wu G (2017b) Monitoring depth of anesthesia using detrended fluctuation analysis based on EEG signals. J Med Biol Eng 37:171–180Google Scholar
  30. Liang Z, Wang Y, Sun X, Li D, Voss LJ, Sleigh JW, Hagihira S, Li X (2015) EEG entropy measures in anesthesia. Front Comput Neurosci 18:9–16Google Scholar
  31. Liang Z, Huang C, Li Y, Hight DF, Voss LJ, Sleigh JW, Li X, Bai Y (2018) Emergence EEG pattern classification in sevoflurane anesthesia. Physiol Meas 39(4):045006. CrossRefPubMedGoogle Scholar
  32. Liu Q, Chen YF, Fan SZ, Abbod M, Shieh JS (2017a) Quasi-periodicities detection using phase-rectified signal averaging in EEG signals as a depth of anesthesia monitor. IEEE Trans Neural Syst Rehabil Eng 2:1–13Google Scholar
  33. Liu Q, Chen YF, Fan SZ, Abbod MF, Shieh JS (2017b) Improved spectrum analysis in EEG for measure of depth of anesthesia based on phase-rectified signal averaging. Physiol Meas 38:116–138PubMedGoogle Scholar
  34. Mateos DM, Guevara Erra R, Wennberg R, Perez Velazquez JL (2018) Measures of entropy and complexity in altered states of consciousness. Cogn Neurodyn 12:73–84PubMedGoogle Scholar
  35. Mckay ID, Voss LJ, Sleigh JW, Barnard JP, Johannsen EK (2006) Pharmacokinetic pharmacodynamic modeling the hypnotic effect of sevoflurane using the spectral entropy of the electroencephalogram. Anesth Analg 102:91–97PubMedGoogle Scholar
  36. Mhuircheartaigh RN, Rosenorn-Lanng D, Wise R et al (2010) Cortical and subcortical connectivity changes during decreasing levels of consciousness in humans: a functional magnetic resonance imaging study using propofol. J Neurosci 30:9095–9102PubMedPubMedCentralGoogle Scholar
  37. Mohammadpoory Z, Nasrolahzadeh M, Mahmoodian N, Sayyah M, Haddadnia J (2019) Complex network based models of ECoG signals for detection of induced epileptic seizures in rats. Cogn Neurodyn 13:325–339PubMedGoogle Scholar
  38. Monk TG, Saini V, Weldon BC, Sigl JC (2005) Anesthetic management and one-year mortality after noncardiac surgery. Anesth Analg 100:4–10PubMedGoogle Scholar
  39. Mumtaz W, Vuong PL, Xia L, Malik AS, Rashid RBA (2017) An EEG-based machine learning method to screen alcohol use disorder. Cogn Neurodyn 11:161–171PubMedGoogle Scholar
  40. Nallasamy N, Tsao DY (2011) Functional connectivity in the brain: effects of anesthesia. Neuroscientist 17(1):94–106PubMedGoogle Scholar
  41. Nguyen-Ky T, Wen P, Li Y (2010) An improving detrended moving-average method for monitoring the depth of anaesthesia. IEEE Trans Biomed Eng 57:2369–2378PubMedGoogle Scholar
  42. Nguyen-Ky T, Wen P, Li Y (2013) Consciousness and depth of anesthesia assessment based on Bayesian analysis of EEG signals. IEEE Trans Biomed Eng 60:1488–1498PubMedGoogle Scholar
  43. Nguyen-Ky T, Wen P, Li Y (2014) Monitoring the depth of anaesthesia using Hurst exponent and Bayesian methods. IET Signal Process 8:907–917Google Scholar
  44. Nicolaou N, Georgiou J (2014) Spatial analytic phase difference of EEG activity during anesthetic-induced unconsciousness. Clin Neurophysiol 125(10):2122–2131PubMedGoogle Scholar
  45. Pal D, Silverstein BH, Sharba L, Li D, Hambrecht-Wiedbusch VS, Hudetz AG, Mashour GA (2017) Propofol, sevoflurane, and ketamine induce a reversible increase in delta-gamma and theta-gamma phase-amplitude coupling in frontal cortex of rat. Front Syst Neurosci 14:41Google Scholar
  46. Palus M (1996) Coarse-grained entropy rates for characterization of complex time series. Physica D 93:64–77Google Scholar
  47. Pereda E, Quiroga RQ, Bhattacharya J (2005) Nonlinear multivariate analysis of neurophysiological signals. Prog Neurobiol 77(1):1–37PubMedGoogle Scholar
  48. Pilge S, Zanner R, Schneider G, Blum J, Kreuzer M, Kochs EF (2006) Time delay of index calculation: analysis of cerebral state, bispectral, and narcotrend indices. Anesthesiology 104:488–494PubMedGoogle Scholar
  49. Rampil IJ (1998) A primer for EEG signal processing in anesthesia. Anesthesiology 89:980–1002PubMedGoogle Scholar
  50. Saadeh W, Khan F, Altaf MAB (2019) Design and implementation of a machine learning based EEG processor for accurate estimation of depth of anesthesia. IEEE Trans Biomed Circuits Syst 13:658–669PubMedGoogle Scholar
  51. Schrouff J, Perlbarg V, Boly M et al (2011) Brain functional integration decreases during propofol-induced loss of consciousness. Neuroimage 57:198–205PubMedGoogle Scholar
  52. Sebel PS, Bowdle TA, Ghoneim MM, Rampil IJ, Padilla RE, Gan TJ, Domino KB (2004) The incidence of awareness during anesthesia: a multicenter United States study. Anesth Analg 99:833–839Google Scholar
  53. Shalbaf R, Behnam H, Sleigh JW, Voss LJ (2012a) Using the Hilbert–Huang transform to measure the electroencephalographic effect of propofol. Physiol Meas 33:271–285PubMedGoogle Scholar
  54. Shalbaf R, Behnam H, Sleigh JW, Voss LJ (2012b) Measuring the effects of sevoflurane on electroencephalogram using sample entropy. Acta Anaesthesiol Scand 56:880–889PubMedGoogle Scholar
  55. Shalbaf R, Behnam H, Sleigh JW, Steyn-Ross A, Voss LJ (2013) Monitoring the depth of anesthesia using entropy features and an artificial neural network. J Neurosci Methods 218:17–24PubMedGoogle Scholar
  56. Shalbaf A, Saffar M, Sleigh JW, Shalbaf R (2018) Monitoring the depth of anesthesia using a new adaptive neuro-fuzzy system. IEEE J Biomed Health Inform 22:671–677PubMedGoogle Scholar
  57. Shalbaf A, Shalbaf R, Saffar M et al (2019) Monitoring the level of hypnosis using a hierarchical SVM system. J Clin Monit Comput. CrossRefPubMedGoogle Scholar
  58. Sleigh JW, Vizuete JA, Voss L, Steyn-Ross A, Steyn-Ross M et al (2009) The electrocortical effects of enflurane: experiment and theory. Anesth Analg 109:1253–1262PubMedPubMedCentralGoogle Scholar
  59. Smith WD, Dutton RC, Smith NT (1996) Measuring the performance of anesthetic depth indicators. Anesthesiology 84(1):38–51PubMedGoogle Scholar
  60. Stam CJ (2005) Nonlinear dynamical analysis of EEG and MEG: review of an emerging field. Clin Neurophysiol 116(10):2266–2301PubMedGoogle Scholar
  61. Talebi N, Nasrabadi AM, Mohammad-Rezazadeh I (2018) Estimation of effective connectivity using multi-layer perceptron artificial neural network. Cogn Neurodyn 12:21–42PubMedGoogle Scholar
  62. Williams ML, Sleigh JW (1999) Auditory recall and response to command during recovery from Propofol anaesthesia. Anaesth Intensive Care 27:265–268PubMedGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  • Fahimeh Afshani
    • 1
  • Ahmad Shalbaf
    • 2
    Email author
  • Reza Shalbaf
    • 3
  • Jamie Sleigh
    • 4
  1. 1.Department of Biomedical Engineering, Electronic BranchIslamic Azad UniversityTehranIran
  2. 2.Department of Biomedical Engineering and Medical Physics, School of MedicineShahid Beheshti University of Medical SciencesTehranIran
  3. 3.Institute for Cognitive Science StudiesTehranIran
  4. 4.Department of AnesthesiaWaikato HospitalHamiltonNew Zealand

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