Izvestiya of Saratov University.

Physics

ISSN 1817-3020 (Print)
ISSN 2542-193X (Online)

For citation:

Navrotskaya E. V., Karavaev A. S., Sinkin M. V., Borovkova E. I., Bezruchko B. P. Adaptation of the method of coupling analysis based on phase dynamics modeling to EEG signals during an epileptic seizure in comatose patients. Izvestiya of Saratov University. Physics , 2022, vol. 22, iss. 1, pp. 4-14. DOI: 10.18500/1817-3020-2022-22-1-4-14, EDN: FBZGDC

This is an open access article distributed under the terms of Creative Commons Attribution 4.0 International License (CC-BY 4.0).
Published online: 
31.03.2022
Full text:
download
(downloads: 339)
Language: 
Russian
Heading: 
Biophysics and medical physics
Article type: 
Article
UDC: 
530.18
DOI: 
10.18500/1817-3020-2022-22-1-4-14
EDN: 
FBZGDC

Adaptation of the method of coupling analysis based on phase dynamics modeling to EEG signals during an epileptic seizure in comatose patients

Autors: 
Navrotskaya Elena Vladimirovna, Saratov State University
Karavaev Anatoly Sergeevich, Saratov State University
Sinkin Mikhail V., Scientific Research Institute of First Aid named after N.V. Sklifosovsky
Borovkova Ekaterina Igorevna, Saratov State University
Bezruchko Boris Petrovich, Saratov State University
Abstract: 

Background and Objectives: the coupling of EEG signals during an epileptic seizure in patients during coma is being studied. Materials and Methods: the analysis of the applicability of the method of detecting the interaction between oscillatory systems based on the phase dynamics modeling to EEG signals during an epilepsy seizure in comatose patients is carried out. Results: a method of preliminary filtering of EEG signals has been proposed and the values of the method parameters have been selected, which allow obtaining reliable estimates of directional coupling at a significance level of 0.05. As an example, the analysis of the couplings between EEG signals of two patients with the mentioned pathologies was carried out using the method of the coupling estimation developed in this work. 

Key words: 
EEG
epilepsy
coma
phase
phase dynamics modeling
coupling estimation
coupling direction
statistical significance
time series analysis
mean phase coherence
Acknowledgments: 
The reported study was funded by RFBR according to the research project No. 18-29-02035, by the Grant Council of the President of the Russian Federation for the state support of young Russian scientists – candidates of sciences (project No. MK-2325.2021.1.2) and in the framework of the state task of Saratov Branch of the Institute of Radioengineering and Electronics of the Russian Academy of Sciences.
Reference: 
  1. Bezruchko B. P., Ponomarenko V. I., Prokhorov M. D., Smirnov D. A., Tass P. A. Modeling nonlinear oscillatory systems and diagnostics of coupling between them using chaotic time series analysis : Applications in neurophysiology. Phys. Usp., 2008, vol. 51, pp. 304–310. https://www.doi.org/10.1070/PU2008v051n03ABEH00649
  2. Lehnertz K., Bialonski S., Horstmann M., Krug D., Rothkegel A., Staniek M., Wagner T. Synchronization phenomena in human epileptic brain networks. Journal of Neuroscience Methods, 2009, vol. 183, pp. 42–48. https://www.doi.org/10.1016/j.jneumeth.2009.05.015
  3. Nini A., Feingold A., Slovin H., Bergman H. Neurons in the globus pallidus do not show correlated activity in the normal monkey, but phase-locked oscillations appear in the MPTP model of parkinsonism. J. Neurophysiol., 1995, vol. 74, pp. 1800–1805. https://www.doi.org/0.1152/jn.1995.74.4.1800
  4. Sysoeva M. V., Sysoev I. V. Mathematical modeling of encephalogram dynamics during epileptic seizure. Technical Physics Letters, 2012, vol. 38, no. 2, pp. 151–154. https://www.doi.org/10.1134/S1063785012020137
  5. Sysoeva M. V., Luttjohann A., Luijtelaar G. van, Sysoev I. V. Dynamics of directional coupling underlying spike-wave discharges. Neuroscience, 2016, vol. 314, pp. 75–89. https://www.doi.org/10.1016/j.neuroscience.2015.11.044
  6. Sysoeva M. V., Kuznetsova G. D., Sysoev I. V. The modeling of rat EEG signals in absence epilepsy in the analysis of brain connectivity. Biophysics, 2016, vol. 61, no. 4, pp. 661–669. https://www.doi.org/10.1134/S0006350916040230
  7. Grishchenko A. A., van Rijn C. M., Sysoev I. V. Comparative analysis of methods for estimation of undirected coupling from time series of intracranial EEGs of cortex of rats-genetic models of absence epilepsy. Matematicheskaya biologiya i bioinformatika [Mathematical Biology and Bioinformatics], 2017, vol. 12, no. 2, pp. 317–326. https://www.doi.org/10.17537/2017.12.317
  8. Basti A., Pizzella V., Chella F., Marzetti L. Disclosing brain functional connectivity from electrophysiological signals with phase slope based metrics. Journal of the Serbian Society for Computational Mechanics, 2017, vol. 11, pp. 50–62. https://www.doi.org/10.24874/jsscm. 2017.11.02.05
  9. Navrotskaya E. V., Alipov V. V., Ishbulatov Yu. M., Bezruchko B. P., Zeulina E. E., Kuligin A. V., Sadchikov D. V. Estimating the influence of spinal block and ataractanalgesia on the coupling between the rhythms of autonomic control of heart rate and vascular tone during gynecological operation. Russian Open Medical Journal, 2019, vol. 8, no. 3, article number e0305. https://www.doi.org/10.15275/rusomj.2019.0305
  10. Hinrichs H., Noesselt T., Heinze H.-J. Directed information flow – a model free measure to analyze causal interactions in event related EEG-MEG-experiments. Human Brain Mapping, 2008, vol. 29, pp. 193–206. https://www.doi.org/10.1002/hbm.20382
  11. Quian Quiroga R., Kraskov A., Kreuz T., Grassberger P. On the performance of different synchronization measures in real data : A case study on electroencephalographic signals. Physical Review E, 2000, vol. 65, article number 041903. https://www.doi.org/10.1103/PhysRevE.65.041903
  12. Kreuz T., Andrzejak R., Mormann F., Kraskov A., Stögbauer Harald, Elger C., Lehnertz K., Grassberger P. Measure profile surrogates : A method to validate the performance of epileptic seizure prediction algorithms. Physical Review E, 2004, vol. 69, article number 061915. https://www.doi.org/10.1103/PHYSREV~E.69.061915
  13. Mormann F., Kreuz T., Rieke C., Andrzejak R., Kraskov A., David P., Elger C., Lehnertz K. On the predictability of epileptic seizures. Clinical Neurophysiology, 2005, vol. 116, pp. 569–587. https://www.doi.org/10.1016/j.clinph.2004.08.025
  14. Brea J., Russell D. F., Neiman A. B. Measuring direction in the coupling of biological oscillators : A case study for electroreceptors of paddlefish. Chaos, 2006, vol. 16, article number 026111. https://www.doi.org/10.1063/1.2201466
  15. Baccala L., Sameshima K., Ballester G., Valle A., TimoIaria C. Studying the Interaction Between Brain Structures via Directed Coherence and Granger Causality. Applied Signal Processing, 1998, vol. 5, pp. 40–48. https://www.doi.org/10.1007/s005290050005
  16. Rosenblum M. G., Pikovsky A. S. Detecting direction of coupling in interacting oscillators. Phys. Rev. E, 2001, vol. 64, pp. 045202. https://www.doi.org/10.1103/PhysRevE.64.045202
  17. Smirnov D., Bezruchko B. Estimation of interaction strength and direction from short and noisy time series. Phys. Rev. E, 2003, vol. 68, article number 046209. https://www.doi.org/10.1103/PhysRevE.68.046209
  18. Smirnov D. A., Bodrov M. B., Bezruchko B. P. Estimation of Coupling Between Oscillators From Time Series Via Phase Dynamics Modeling : Limits Of Method’s Applicability. Izvestiya VUZ. Applied Nonlinear Dynamics, 2004, vol. 12, no. 6, pp. 79–92 (in Russian).
  19. Sidak E. V., Smirnov D. A., Bezruchko B. P. Estimation Of Characteristics Of Delayed Coupling Between Stochastic Oscillators From The Observed Phase Dynamics. Radiophysics and Quantum Electronics, 2015, vol. 58, no. 7, pp. 529–540. https://www.doi.org/10.1007/s11141-015-9626-x
  20. Sidak E. V., Smirnov D. A., Osipov G. V., Bezruchko B. P. Influence of nonlinear amplitude dynamics on estimated delay time of coupling between self-oscillatory systems. Technical Physics Letters, 2016, vol. 42, pp. 287–290. https://www.doi.org/10.1134/S1063785016030317
  21. Khorev V. S., Kiselev A. R., Shvartz V. A., Lapsheva E. E., Ponomarenko V. I., Prokhorov M. D., Gridnev V. I., Karavaev A. S. Investigation of Delay Time in Interaction between the Regulatory Circuits in the Cardiovascular System of Healthy Humans Using Modeling of Phase Dynamics. Izvestiya of Saratov University. Physics, 2016, vol. 16, iss. 4, pp. 227–237. (in Russian). https://www.doi.org/10.18500/1817-3020-2016-16-4-227-237
  22. Baboukani S., Azemi G., Boashash B., Colditz P., Omidvarnia A. A novel multivariate phase synchrony measure : Application to multichannel newborn EEG analysis. Digital Signal Processing, 2018, vol. 84, article number 57380071. https://www.doi.org/10.1016/j.dsp.2018.08.019
  23. Stankovski T., Pereira T., McClintock P., Stefanovska A. Coupling functions : Dynamical interaction mechanisms in the physical, biological and social sciences. Philosophical Transactions of the Royal Society A : Mathematical, Physical and Engineering Sciences, 2019, vol. 377, article number 20190039. https://www.doi.org/10.1098/rsta.2019.0039  
  24. Musizza B., Stefanovska A., McClintock P., Palus M., Petrovcic J., Ribaric S., Bajrovic F. Interactions between cardiac, respiratory and EEG-δ oscillations in rats during anaesthesia. J. Physiol., 2007, vol. 580, pp. 315–326. https://www.doi.org/10.1113/jphysiol.2006.126748
  25. Gruszecka A., Nuckowska M., Waskow M., Kot J., Winklewski P., Guminski W., Frydrychowski A., Wtorek J., Bujnowski A., Lass P., Stankovski T., Gruszecki M. Coupling between Blood Pressure and Subarachnoid Space Width Oscillations during Slow Breathing. Entropy, 2021, vol. 23, article number 113. https://www.doi.org/10.3390/e23010113  
  26. Kamali S., Gharibzadeh S., Jafari S. A new look to coma from the viewpoint of nonlinear dynamics. Nonlinear Dyn., 2018, vol. 92, pp. 2119–2131. https://www.doi.org/10.1007/s11071-018-4184-3
Received: 
01.10.2021
Accepted: 
02.12.2021
Published: 
31.03.2022
  • 1091 reads