Advertisement

Application of Quantile Graphs to the Automated Analysis of EEG Signals

  • Andriana S. L. O. Campanharo
  • Erwin Doescher
  • Fernando M. Ramos
Article
  • 56 Downloads

Abstract

Epilepsy is classified as a chronic neurological disorder of the brain and affects approximately 2% of the world population. This disorder leads to a reduction in people’s productivity and imposes restrictions on their daily lives. Studies of epilepsy often rely on electroencephalogram (EEG) signals to provide information on the behavior of the brain during seizures. Recently, a map from a time series to a network has been proposed and that is based on the concept of transition probabilities; the series results in a so-called “quantile graph” (QG). Here, this map, which is also called the QG method, is applied for the automatic detection of normal, pre-ictal (preceding a seizure), and ictal (occurring during a seizure) conditions from recorded EEG signals. Our main goal is to illustrate how the differences in dynamics in the EEG signals are reflected in the topology of the corresponding QGs. Based on various network metrics, namely, the clustering coefficient, the shortest path length, the mean jump length, the modularity and the betweenness centrality, our results show that the QG method is able to detect differences in dynamical properties of brain electrical activity from different extracranial and intracranial recording regions and from different physiological and pathological brain states.

Keywords

Electroencephalographic time series Epilepsy Complex networks Quantile graphs Network measures 

Notes

Acknowledgements

A. S. L. O. C. acknowledges the support of FAPESP: 2013/19905-3 and 2017/05755-0. All figures were generated with PyGrace (http://pygrace.github.io/) with color schemes from  Colorbrewer (http://colorbrewer.org).

References

  1. 1.
    Acharya UR, Faust O, Kannathal N, Chua T, Laxminarayan S (2005) Non-linear analysis of EEG signals at various sleep stages. Comput Methods Prog Biomed 80:37–45CrossRefGoogle Scholar
  2. 2.
    Acharya UR, Molinari F, Sree SV, Chattopadhyav S, Ng KH, Suri JS (2012) Automated diagnosis of epileptic EEG using entropies. Biomed Signal Process Control 7:401–408CrossRefGoogle Scholar
  3. 3.
    Al-Fahoum AS, Al-Fraihat AA (2014) Methods of EEG signal features extraction using linear analysis in frequency and time-frequency domains. ISRN Neuroscience 2014Google Scholar
  4. 4.
    Albert R, Barabási AL (2002) Statistical mechanics of complex networks. Rev Mod Phys 74:47MathSciNetCrossRefGoogle Scholar
  5. 5.
    Alotaiby TN, Alshebeili SA, Alshawi T, Ahmad I, El-samie FEA (2014) EEG seizure detection and prediction algorithms: a survey. EURASIP J Adv Signal Process 2014:183CrossRefGoogle Scholar
  6. 6.
    Anderson NR, Doolittle LM (2010) Automated analysis of EEG: opportunities and pitfalls. J Clin Neurophysiol 27:453–457CrossRefGoogle Scholar
  7. 7.
    Andrzejak RG, Widman G, Lehnertz K, Rieke C, David P, Elger CE (2001) The epileptic process as nonlinear deterministic dynamics in a stochastic environment: an evaluation on mesial temporal lobe epilepsy. Epilepsy Res 44:129–140CrossRefGoogle Scholar
  8. 8.
    Andrzejak RG, Schindler K, Rummel C (2012) Nonrandomness, nonlinear dependence, and nonstationarity of electroencephalographic recordings from epilepsy patients. Phys Rev E 86:046206CrossRefGoogle Scholar
  9. 9.
    Campanharo ASLO, Doescher E, Ramos FM Automated EEG (2017) signals analysis using quantile graphs. In: Rojas I, Joya G, Catala A (eds) Advances in computational intelligence. IWANN 2017. Lecture notes in computer science, vol 10306. Springer, BerlinGoogle Scholar
  10. 10.
    Campanharo ASLO, Ramos FM (2016) Hurst exponent estimation of self-affine time series using quantile graphs. Phys A 444:43–48CrossRefGoogle Scholar
  11. 11.
    Campanharo ASLO, Ramos FM (2016) Quantile graphs for the characterization of chaotic dynamics in time series. In: WCCS 2015—IEEE third world conference on complex systems. IEEEGoogle Scholar
  12. 12.
    Campanharo ASLO, Ramos FM (2017) Distinguishing different dynamics in electroencephalographic time series through a complex network approach. In: Proceeding series of the Brazilian Society of Applied and Computational Mathematics, vol 5. SBMACGoogle Scholar
  13. 13.
    Campanharo ASLO, Sirer MI, Malmgren RD, Ramos FM, Amaral LAN (2011) Duality between time series and networks. PLoS ONE 6:e23378CrossRefGoogle Scholar
  14. 14.
    Costa LF, Rodrigues FA, Travieso G, Villas PR (2007) Characterization of complex networks. Adv Phys 56:167–242CrossRefGoogle Scholar
  15. 15.
    Fagiolo G (2007) Clustering in complex directed networks. Phys Rev E 76:026107CrossRefGoogle Scholar
  16. 16.
    Faust O, Acharya RU, Allen AR, Lin C (2007) Analysis of EEG signals during epileptic and alcoholic states using ar modeling techniques. Innov Res BioMed Eng 29:44–52Google Scholar
  17. 17.
    Freeman LC (1977) A set of measures of centrality based on betweenness. Sociometry 40:35–41CrossRefGoogle Scholar
  18. 18.
    Gadhoumi K, Lina JM, Gotman J (2012) Discriminating preictal and interictal states in patients with temporal lobe epilepsy using wavelet analysis of intracerebral EEG. Clin Neurophysiol 123:1906–1916CrossRefGoogle Scholar
  19. 19.
    Gandhi T, Panigrahi BK, Anand S (2011) A comparative study of wavelet families for EEG signal classification. Neurocomputing 74:3051–3057CrossRefGoogle Scholar
  20. 20.
    Gevins AS, Yeager CL, Diamond SL, Spire J, Zeitlin GM, Gevins AH (1975) Automated analysis of the electrical activity of the human brain (EEG): a progress report. In: Proceedings of the IEEE, vol 63. IEEEGoogle Scholar
  21. 21.
    Guimerà R, Amaral LAN (2005) Cartography of complex networks: modules and universal roles. J Stat Mech Theory Exp  https://doi.org/10.1088/1742-5468/2005/02/P02001 CrossRefGoogle Scholar
  22. 22.
    Güler I, Übeyli ED (2007) Expert systems for time-varying biomedical signals using eigenvector methods. Expert Syst Appl 32:1045–1058CrossRefGoogle Scholar
  23. 23.
    Guo L, Rivero D, Pazos A (2010) Epileptic seizure detection using multiwavelet transform based approximate entropy and artificial neural networks. J Neurosci Methods 193:156–163CrossRefGoogle Scholar
  24. 24.
    Hajian-Tilaki K (2013) Receiver operating characteristic (ROC) curve analysis for medical diagnostic test evaluation. Casp J Intern Med 4:627Google Scholar
  25. 25.
    Khamis H, Mohamed A, Simpson S (2013) Frequency-moment signatures: a method for automated seizure detection from scalp EEG. Clin Neurophysiol 124:2317–2327CrossRefGoogle Scholar
  26. 26.
    Liu Y, Zhou W, Yuan Q, Chen S (2012) Automatic seizure detection using wavelet transform and SVM in long-term intracranial EEG. EEE Trans Neural Syst Rehabil Eng 20:749–755CrossRefGoogle Scholar
  27. 27.
    Morris AS, Langari R (2012) Measurement and instrumentation. Academic Press, San DiegoGoogle Scholar
  28. 28.
    Musselman MW, Djurdjanovic D (2012) Time-frequency distributions in the classification of epilepsy from EEG signals. Expert Syst Appl 39:11413–11422CrossRefGoogle Scholar
  29. 29.
    Nasehi S, Pourghassem H (2012) Seizure detection algorithms based on analysis of EEG and ECG signals: a survey. Neurophysiology 44:174–186CrossRefGoogle Scholar
  30. 30.
    Newman M (2010) Networks: an introduction. Oxford University Press, New YorkCrossRefGoogle Scholar
  31. 31.
    Newman MEJ (2003) The structure and function of complex networks. SIAM Rev 45:167–256MathSciNetCrossRefGoogle Scholar
  32. 32.
    Newman MEJ (2006) Modularity and community structure in networks. Proc Natl Acad Sci USA 103:8577–8582CrossRefGoogle Scholar
  33. 33.
    Obuchowski NA, Bullen J (2018) Receiver operating characteristic (ROC) curves: review of methods with applications in diagnostic medicine. Phys Med Biol 63:07TR01CrossRefGoogle Scholar
  34. 34.
    Rana P, Lipor J, Lee H, Van Drongelen W, Kohrman MH, Van Veen B (2012) Seizure detection using the phase-slope index and multichannel ECoG. IEEE Trans Biomed Eng 59:1125–1134CrossRefGoogle Scholar
  35. 35.
    Ridouh A, Boutana D, Bourennane S (2017) EEG signals classification based on time frequency analysis. J Circuits Syst Comput 26:1750198CrossRefGoogle Scholar
  36. 36.
    Sales-Pardo M, Guimerà R, Amaral LAN (2007) Extracting the hierarchical organization of complex systems. Proc Natl Acad Sci USA 104:15224–15229CrossRefGoogle Scholar
  37. 37.
    Santos-Mayo L, San-José-Revuelta L, Arribas JI (2016) A computer-aided diagnosis system with EEG based on the p3b wave during an auditory odd-ball task in schizophrenia. In: IEEE transactions on biomedical engineering, vol 64. IEEEGoogle Scholar
  38. 38.
    Seizures and epilepsy: Hope through research. www page (2004). http://www.ninds.nih.gov/disorders/epilepsy/detail_epilepsy.htm
  39. 39.
    Tsolaki A, Kazis D, Kompatsiaris I, Kosmidou V, Tsolaki M (2014) Electroencephalogram and Alzheimer’s disease: clinical and research approaches. Int J Alzheimer’s Dis  https://doi.org/10.1155/2014/349249 CrossRefGoogle Scholar
  40. 40.
    Ubeyli ED (2011) Analysis of EEG signals by combining eigenvector methods and multiclass support vector machines. Comput Biol Med 38:14–22CrossRefGoogle Scholar
  41. 41.
    Ubeyli ED, Guler I (2007) Features extracted by eigenvector methods for detecting variability of EEG signals. Comput Biol Med 28:592–603Google Scholar
  42. 42.
    Xia Y, Zhou W, Li C, Yuan Q, Geng S (2015) Seizure detection approach using S-transform and singular-value decomposition. Epilep Behav 52:187–193CrossRefGoogle Scholar
  43. 43.
    Zar JH (2010) Biostatistical analysis. Prentice Hall, New JerseyGoogle Scholar
  44. 44.
    Zhang Y, Liu B, Ji X, Huang D (2016) Classification of EEG signals based on autoregressive model and wavelet packet decomposition. Neural Process Lett 45:365–378CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Departamento de Bioestatística, Instituto de BiociênciasUniversidade Estadual PaulistaBotucatuBrazil
  2. 2.Departamento de Ciência e TecnologiaUniversidade Federal de São PauloSão PauloBrazil
  3. 3.Laboratório de Computação e Matemática AplicadaInstituto Nacional de Pesquisas EspaciaisSão PauloBrazil

Personalised recommendations