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Segmentation of quasiperiodic patterns in EEG recordings for analysis of post-traumatic paroxysmal activity in rat brains

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Abstract

The study of the consequences of traumatic brain injury represents an important problem in modern medicine. Brain trauma leads to the manifestation of neurological and psychiatric disturbances in most patients; severe traumatic brain injuries are accompanied by paroxysms and neurologic impairment, which in the long term lead to post-traumatic epilepsy. The mechanisms of post-traumatic epilepsy, in view of cellular and molecular plasticity, include rearrangement of neuronal networks. These processes induce spontaneous and synchronous discharges of numerous neurons, clinically manifested in the form of paroxysms. The posttraumatic period before the first unprovoked seizures is known as the latent phase of post-traumatic epileptogenesis, which can last for years in humans. Unfortunately, approaches to its detection and to the prediction of post-traumatic epilepsy have not been sufficiently developed yet; though they are of exceptional importance for clinical practice. Moreover, it is still poorly understood how the damage of the brain tissue leads to the brain structural rearrangement connected with epileptogenesis. In other words, the question is why and when the epileptogenesis occurs. The current paper considers the first results of the approach that we have suggested to quantitative assessment of the epileptogenesis development on the basis of EEG analysis as an alternative to the regular activity of the brain. This approach is based on the analysis of highly nonstationary signals that contain quasiperiodic fragments. In particular, the pipeline of procedures for the analysis includes detection of local quasiperiodic (rhythmic) behavior with further assessment of its dynamic characteristics and segmentation of fragments with “rhythmic” behavior. The first results of this approach application are connected with the analysis of experimental data obtained in the modeling of traumatic brain injury in rats. The characteristics of the EEG rhythm and their dynamics are analyzed in the background (before traumatic brain injury) and one, two, three and six days after the injury on the basis of ten-hour EEG recordings. The results demonstrate a significant difference in the degree of periodicity of the characteristics (total duration of the corresponding EEG fragments) before and after traumatic brain injury (a week after the trauma some restoration of the periodicity is observed). At the end of the paper we offer some interpretation of these results and express a hope that such studies could provide new ways for development and assessment of methods of the paroxysmal activity in the post-traumatic period.

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References

  1. J. F. Annegers, W. A. Hauser, et al., “A populationbased study of seizures after traumatic brain injuries,” N. Engl. J. Med. 338 (1), 20–24 (1998).

    Article  Google Scholar 

  2. A. Pitkanen and J. Engel, Jr., “Past and present definitions of epileptogenesis and its biomarkers,” Neurotherapeutics 11 (2), 231–241 (2014).

    Article  Google Scholar 

  3. K. Obukhov, I. Kershner, I. Komoltsev, et al., “An approach for EEG of post traumatic sleep spindles and epilepsy seizures detection and classification in rats,” Pattern Recogn. Image Anal. 27 (1), 114–121 (2017).

    Article  Google Scholar 

  4. D. B. Arciniegas, C. A. Anderson, et al., “Mild traumatic brain injury: a neuropsychiatric approach to diagnosis, evaluation and treatment,” Neuropsychiatr. Dis. Treat. 1 (4), 311–327 (2005).

    Google Scholar 

  5. I. Komoltsev, I. Levshina, et al., “Anxiety in acute posttraumatic period is associated with reduced remsleep in rats,” J. Higher Nervous Activity 67 (2), 1–15 (2017).

    Google Scholar 

  6. N. Grima, J. Ponsford, et al., “Sleep disturbances in traumatic brain injury: a meta-analysis,” J. Clin. Sleep Med. 12 (3), 419–428 (2016).

    Article  Google Scholar 

  7. E. T. Hannah, Y. F. Heidi, M. R. Kristyn, et al., “Hypocretinergic and cholinergic contributions to sleep-wake disturbances in a mouse model of traumatic brain injury,” Neurobiol. Sleep Circadian Rhythms 2, 71–84 (2017).

    Article  Google Scholar 

  8. H. Adeli, S. Ghosh-Dastidar, and N. Dadmehr, “A wavelet-chaos methodology for analysis of EEGs and EEG subbands to detect seizure and epilepsy,” IEEE Trans. Biomed. Eng. 54 (2), 205–211 (2007).

    Article  Google Scholar 

  9. W. J. Williams, et al., “Time-frequency analysis of electrophysiology signals in epilepsy,” IEEE Eng. Med. Biol. 14 (2), 133–143 (1995).

    Article  Google Scholar 

  10. V. E. Antsiperov, “Multiscale correlation analysis of nonstationary signals containing quasi-periodic fragments,” J. Commun. Technol. Electron. 53 (1), 65–77 (2008).

    Article  Google Scholar 

  11. V. E. Antsiperov, “Human brain rhythms detection based on correlation of EEG analytical spectra in the main frequency bands,” J Radio Electron., No. 5 (2014). http://jre.cplire.ru/jre/may14/13/text.pdf.

  12. V. R. McIntosh, L. Noble, et al., “Traumatic brain injury in the rat: characterization of a lateral fluid percussion model,” Neuroscience 28 (1), 233–244 (1989).

    Article  Google Scholar 

  13. A. Pitkanen and R. Immonen, “Epilepsy related to traumatic brain injury,” Neurotherapeutics 11 (2), 286–296 (2014).

    Article  Google Scholar 

  14. H. J. Thompson, J. Lifshitz, et al., “Lateral fluid percussion brain injury: a 15-year review and evaluation,” J. Neurotrauma 22 (1), 42–75 (2005).

    Article  Google Scholar 

  15. J. Alder, W. Fujioka, et al., “Lateral fluid percussion: model of traumatic brain injury in mice,” J. Vis. Exp. 54, e3063 (2011).

    Google Scholar 

  16. V. E. Antsiperov and Y. V. Obukhov, “Epileptic seizure detection and segmentation based on multiscale correlation analysis of EEG recordings,” in Proc. 3rd Eurasian Congress on Medical Physics and Engineering “Medical Physics–2010” (Moscow State Univ., Moscow, 2010), Vol. 3, pp. 188–190.

    Google Scholar 

  17. Time-Frequency Analysis: Concepts and Methods, Ed. by F. Hlawatsch and F. Auger (ISTE and Wiley, London, 2008).

  18. J. Jeong and W. J. Williams, “Kernel design for reduced interference distributions,” IEEE Trans. Signal Processing 40 (2), 402–412 (1992).

    Article  Google Scholar 

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Authors and Affiliations

  1. Kotel’nikov Institute of Radio Engineering and Electronics, Russian Academy of Sciences, Moscow, Russia

    V. E. Antsiperov & Yu. V. Obukhov

  2. Institute of Higher Nervous Activity and Neurophysiology Russian Academy of Sciences, Moscow, Russia

    I. G. Komol’tsev & N. V. Gulyaeva

Authors
  1. V. E. Antsiperov
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  2. Yu. V. Obukhov
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  3. I. G. Komol’tsev
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  4. N. V. Gulyaeva
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Correspondence to V. E. Antsiperov.

Additional information

Viacheslav Antsiperov. Born in 1959. Graduated from Moscow Institute of Physics and Technology in 1982. Received candidate’s degree (Physics and Mathematics) in 1986. At present, leading researcher at Kotel’nikov Institute of Radio Engineering and Electronics of Russian Academy of Sciences. Scientific interests: information systems, processing and analysis of signals including image and speech recognition, biomedical informatics. Author of more than 60 papers.

Il’ya Komol’tsev. Born 1991. Graduated from Pirogov Russian National Research Medical University in 2015. Junior research fellow at Institute of Higher Nervous Activity and Neurophysiology, Russian Academy of Sciences. Areas of interest: neurophysiology, EEG, Traumatic Brain Injury.

Yurii Obukhov. Born 1950. Graduated from Moscow Institute of Physics and Technology in 1974. PhD in physics and applied mathematics 1992. Head of laboratory at Kotel’nikov Institute of Radio-Engineering and Electronics of Russian Academy of Sciences. Author of more than 100 scientific publications. Areas of interest: signal processing and analysis, information systems.

Natal’ya Gulyaeva. Professor, PhD in biology. Head of Laboratory of Functional Biochemistry of the Nervous System, Institute of Higher Nervous Activity and Neurophysiology, Russian Academy of Sciences.

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Antsiperov, V.E., Obukhov, Y.V., Komol’tsev, I.G. et al. Segmentation of quasiperiodic patterns in EEG recordings for analysis of post-traumatic paroxysmal activity in rat brains. Pattern Recognit. Image Anal. 27, 789–803 (2017). https://doi.org/10.1134/S1054661817040022

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  • DOI: https://doi.org/10.1134/S1054661817040022

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