ISSN 0869-6632 (Print)
ISSN 2542-1905 (Online)

For citation:

Grubov V. V., Ovchinnikov A. A., Sitnikova E. Y., Koronovskii A. A., Hramov A. E. Wavelet analysis of sleep spindles on EEG and development of method for their automatic diagnostic. Izvestiya VUZ. Applied Nonlinear Dynamics, 2011, vol. 19, iss. 4, pp. 91-108. DOI: 10.18500/0869-6632-2011-19-4-91-108

This is an open access article distributed under the terms of Creative Commons Attribution 4.0 International License (CC-BY 4.0).
Full text PDF(Ru):
(downloads: 270)
Article type: 

Wavelet analysis of sleep spindles on EEG and development of method for their automatic diagnostic

Grubov Vadim Valerevich, Immanuel Kant Baltic Federal University
Ovchinnikov Aleksej Aleksandrovich, Saratov State University
Sitnikova Evgenia Yurievna, Federal State Budgetary Institution of Science "Institute of Higher Nervous Activity and Neurophysiology RAS"
Koronovskii Aleksei Aleksandrovich, Saratov State University
Hramov Aleksandr Evgenevich, Immanuel Kant Baltic Federal University

The detailed wavelet analysis of sleep electric brain activity, obtained from rats with genetic predisposition to absence-epilepsy, has been performed. Characteristic features of time-and-frequency structure of sleep spindles (oscillatory pattern, that serve as electroencephalographic correlate for slow-wave sleep) have been discovered in long-term electroencephalographic data. Operation has been performed using continuous wavelet transform. Few common wavelet bases have been tested and complex Morlet-wavelet turned out to be the most effective for detection of time-and-frequency features of sleep spindles on EEG. Morlet-wavelet has been used for development of system for automatic diagnostic of sleep spindles on EEG. As a result of analysis two types of sleep spindles, that have the same time dynamics, but different frequency structure, have been discovered. Complex dynamics of main frequency during the sleep spindle has been revealed. The method for automatic diagnostic of sleep spindles, based on computation of wavelet transform energy in two frequency ranges for two types of sleep spindles, has been proposed according to obtained data. The testing of method revealed high accuracy of automatic diagnostic for investigating events on EEG. The method can be used in routine EEG researches, related to detection and classification of different oscillatory patterns.

  1. Abarbanel HD, Rabinovich MI, Selverston A, Bazhenov MV, Huerta R, Sushchik MM, Rubchinskii LL. Synchronisation in neural networks. Phys. Usp. 1996;39(4):337–362. DOI: 10.1070/PU1996v039n04ABEH000141.
  2. Mosekilde E, Maistrenko Y, Postnov DE. Chaotic Synchronization, Applications to Living Systems. Singapore: World Scientific; 2002. 440 p. DOI: 10.1142/4845.
  3. Bezruchko BP, Ponomarenko VI, Prokhorov MD, Smirnov DA, Tass PA. Modeling nonlinear oscillatory systems and diagnostics of coupling between them using chaotic time series analysis: applications in neurophysiology. Phys. Usp. 2008;51(3):304–310. DOI: 10.1070/PU2008v051n03ABEH006494.
  4. Nekorkin VI. Nonlinear oscillations and waves in neurodynamics. Phys. Usp. 2008;51(3):295–304. DOI: 10.1070/PU2008v051n03ABEH006493.
  5. Steriade M, Deschenes M. The thalamus as a neuronal oscillator. Brain Res. Rev. 1984;8(1):1–63. DOI: 10.1016/0165-0173(84)90017-1.
  6. Destexhe A, Sejnowski TJ. Thalamocortical Assemblies: How Ion Channels, Single Neurons and Large-Scale Networks Organize Sleep Oscillations. Oxford University Press; 2001. 472 p.
  7. Niedermeyer E, Silva FL. Electroencephalography: Basic Principles, Clinical Applications, and Related Fields. Lippincot Williams & Wilkins; 2004. 1309 p.
  8. Nunez PL, Srinivasan R. Electric Fields of the Brain: The Neurophysics of EEG. Oxford University Press; 1981. 640 p.
  9. Kostopoulos GK. Spike-and-wave discharges of absence seizures as a transformation of sleep spindles: the continuing development of a hypothesis. Clinical Neurophysiology. 2000;111(Suppl 2):S27–S38. DOI: 10.1016/s1388-2457(00)00399-0.
  10. Holschneider M. Wavelets: An Analysis Tool. Oxford University Press; 1995. 448 p.
  11. Aldroubi A, Unser M. Wavelets in Medicine and Biology. CRC-Press; 1996. 632 p.
  12. Daubechies I. Orthonormal bases of compactly supported wavelets. Communications on Pure and Applied Mathematics. 1988;41(7):909–996. DOI: 10.1002/cpa.3160410705.
  13. Tass PA et al. Detection of n:m phase locking from noisy data: Application to magnetoencephalography. Phys. Rev. Lett. 1998;81(15):3291–3294. DOI: 10.1103/PhysRevLett.81.3291.
  14. Tass PA, Fieseler T, Dammers J et al. Synchronization tomography: A method for three-dimensional localization of phase synchronized neuronal populations in the human brain using magnetoencephalography. Phys. Rev. Lett. 2003;90(8):088101. DOI: 10.1103/PhysRevLett.90.088101.
  15. Anishchenko VS, Balanov AG, Janson NB et al. Entrainment between heart rate and weak noninvasive forcing. Int. J. Bifurcat. Chaos. 2000;10(10):2339–2348. DOI: 10.1142/S0218127400001468.
  16. Prokhorov MD, Ponomarenko VI, Gridnev VI et al. Synchronization between main rhythmic processes in the human cardiovascular system. Phys. Rev. E. 2003;68(4):041913. DOI: 10.1103/physreve.68.041913.
  17. Hramov AE, Koronovskii AA, Ponomarenko VI, Prokhorov MD. Detecting synchronization of self-sustained oscillators by external driving with varying frequency. Phys. Rev. E. 2006;73(2):026208. DOI: 10.1103/PhysRevE.73.026208.
  18. Koronovskii AA, Ponomarenko VI, Prokhorov MD, Hramov AE. Method of studying the synchronization of self-sustained oscillations using continuous wavelet analysis of univariant data. Tech. Phys. 2007;52(9):1106–1116. DOI: 10.1134/S1063784207090022.
  19. Meinecke FC, Ziehe A, Kurths J, Muller KR. Measuring phase synchronization of superimposed signals. Phys. Rev. Lett. 2005;94(8):084102. DOI: 10.1103/physrevlett.94.084102.
  20. Chavez M, Adam C, Navarro V et al. On the intrinsic time scales involved in synchronization: A data-driven approach. Chaos. 2005;15(2):023904. DOI: 10.1063/1.1938467.
  21. Velazquez JLP, Khosravani H, Lozano A et al. Type III intermittency in human partial epilepsy. European Journal of Neuroscience. 1999;11(7):2571–2576. DOI: 10.1046/j.1460-9568.1999.00688.x.
  22. Koronovskii AA, Kuznetsova GD, Midzyanovskaya IS, Sitnikova EY, Trubetskov DI, Khramov AE. Universality of intermittent behavior in spontaneous nonconvulsive paroxysmal activity in rats. Proc. Acad. Sci. 2006;409(2):274–276 (in Russian).
  23. Hramov AE, Koronovskii AA, Midzyanovskaya IS et al. On–off intermittency in time series of spontaneous paroxysmal activity in rats with genetic absence epilepsy. Chaos. 2006;16(4):043111. DOI: 10.1063/1.2360505.
  24. Sosnovtseva OV, Pavlov AN, Mosekilde E, Yip KP, Holstein-Rathlou NH, Marsh DJ. Synchronization among mechanisms of renal autoregulation is reduced in hypertensive rats. American Journal of Physiology (Renal Physiology). 2007;293(5):F1545–F1555. DOI: 10.1152/ajprenal.00054.2007.
  25. Sosnovtseva OV, Pavlov AN, Mosekilde E, Holstein-Rathlou NH. Synchronization phenomena in multimode dynamics of coupled nephrons. Izvestiya VUZ. Applied Nonlinear Dynamics. 2003;11(3):133–148 (in Russian).
  26. Koronovskii AA, Minjuhin IM, Tyshenko AA, Hramov AE, Midzjanovskaja IS, Sitnikova EY. Application of continuous wavelet transform to analysis of intermittent behavior. Izvestiya VUZ. Applied Nonlinear Dynamics. 2007;15(4):34–54 (in Russian). DOI: 10.18500/0869-6632-2007-15-4-34-54.
  27. Ovchinnikov AA, Luttjohann A, Hramov AE, van Luijtelaar G. An algorithm for real-time detection of spike-wave discharges in rodents. Journal of Neuroscience Methods. 2010;194(1):172–178. DOI: 10.1016/j.jneumeth.2010.09.017.
  28. Ovchinnikov AA, Hramov AE, Luttjehann A. et al. Method for diagnostics of characteristic patterns of observable time series and its real-time experimental implementation for neurophysiological signals. Tech. Phys. 2011;56(2):1–7. DOI: 10.1134/S1063784211010191.
  29. Koronovskii AA, Khramov AE. Continuous Wavelet Analysis and Its Applications. Moscow: Fizmatlit; 2003. 176 p. (in Russian).
  30. Coenen AM, van Luijtelaar EL. Pharmacological dissociation of EEG and behavior: A basic problem in sleep-wake classification. Sleep. 1991;14(5):464–465.
  31. Mallat SG. Multiresolution approximations and wavelets orthonormal bases of 2 (R). Trans. Amer. Soc. 1989;315(1):69–87.
  32. Paul T. Function analytic on half-plane as quantum mechanical states. J. Math. Phys. 1984;25(11):3252–3263. DOI: 10.1063/1.526072.
  33. Grossman A, Morlet J. Decomposition of hardly functions into square integrable wavelets of constant shape. SIAM J. Math. Anal. 1984;15(4):723–736. DOI: 10.1137/0515056.
  34. Sitnikova EY, Hramov AE, Koronovsky AA, van Luijtelaar G. Sleep spindles and spike–wave discharges in EEG: Their generic features, similarities and distinctions disclosed with Fourier transform and continuous wavelet analysis. Journal of Neuroscience Methods. 2009;180(2):304–316. DOI: 10.1016/j.jneumeth.2009.04.006.
  35. Sitnikova E, van Luijtelaar G. Cortical and thalamic coherence during spike-wave seizures in WAG/Rij rats. Epilepsy Res. 2006;71(2–3):159–180. DOI: 10.1016/j.eplepsyres.2006.06.008.
  36. Abdullayev NT, Dishin OA, Samedova KZ. Automatic classification electroencephalogram on the basis of their wavelet-batch operation. Biomedical Radioelectronics. 2009;(6):63–68 (in Russian).
  37. Abdullayev NT, Dishin OA, Samedova KZ. Wavelet clearing electroencephalogram of artifacts with adaptation to their kind and dynamics. Biomedical Radioelectronics. 2009;(12):47–57 (in Russian).
  38. Bozhokin SV, Suvorov NB. Wavelet analysis of transient electro-encephalogram processes during photostimulation. Biomedical Radioelectronics. 2008;(3):21–25 (in Russian).
  39. Pearson ES, Neyman J. On the Problem of Two Samples. Joint Statistical Papers. Cambridge University Press, Cambridge; 1967.
  40. Raiffa H. Decision Analysis: Introductory Lectures on Choices Under Uncertainty. Addison-Wesley, Reading; 1968. 309 p.
  41. van Luijtelaar G, Hramov AE, Sitnikova EY, Koronovskii AA. Spike-wave discharges in WAG/Rij rats are preceded by delta and theta precursor activity in cortex and thalamus. Clinical Neurophysiology. 2011;122(4):687–695. DOI: 10.1016/j.clinph.2010.10.038. 
Short text (in English):
(downloads: 82)