Advertisement

Electrophysical Properties and Determination of the Impedance of Vestibular Labyrinth Tissues

  • V. P. DemkinEmail author
  • S. V. Melnichuk
  • P. P. Shchetinin
  • H. Kingma
  • R. Van de Berg
Article
  • 3 Downloads

A detailed electric model of current transmission through vestibular labyrinth tissues is suggested based on the anatomic structure of the labyrinth taking into account electrophysical properties of hair and basilar cells of neuroepithelium. Formulas for the impedance of the vestibular organ are derived and phase shifts of the stimulating current are calculated based on experimental data on the electrophysical and anatomic characteristics of vestibular labyrinth tissues of a guinea pig. The dispersion of the impedance is investigated for the frequencies in the range 101–5·104 Hz. It is shown that the phase shift of the current relative to the voltage applied between the electrode and the vestibular nerve is nonmonotonic in character and depends on the frequency. A minimum negative phase shift of the current is observed at f = 200 Hz. Taking into account of the cellular structures of the hair and basilar cells in the electric circuit shows that in the examined frequency range they bring significant contribution to the total impedance. The suggested electric model and the results of calculations can provide the basis for diagnostics of vestibular labyrinth diseases and design of vestibular implants of a new type.

Keywords

electric current impedance of biological tissues electric model vestibular labyrinth vestibular implant 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    O. F. Voropayeva and Yu. I. Shokin, Vychisl. Tekhnol., 17, No. 4, 29–55 (2012).Google Scholar
  2. 2.
    G. I. Marchuk, Medits. Vysok. Tekhnol., No. 2, 3–6 (2012).Google Scholar
  3. 3.
    G. Yu. Riznichenko, Mathematical Models in Biophysics and Ecology [in Russian], Publishing House of Institute of Computer Science, Izhevsk, Moscow (2003).Google Scholar
  4. 4.
    S. V. Antonenko, E. S. Belyanskaya, A. F. Indyukhin, and I. S. Lebedenko, Vestn. Novykh Medicinsk. Tekhnolog. Electronic Edition, No. 1 (2013).Google Scholar
  5. 5.
    A. Lloret-Villas, T. M. Varusai, N. Juty N., et al., Pharmacology, 6, 73–86 (2017).Google Scholar
  6. 6.
    R. Van de Berg, N. Guinand, and T. A. K. Nguyen, Frontiers in System Neyrosciense, 8, 1–12 (2012).Google Scholar
  7. 7.
    D. Jiang, A. Demosthenous, T. A. Perkins, et al., IEEE Trans. Biomed. Circuits and Systems, 5, No. 2, 147–159 (2011).CrossRefGoogle Scholar
  8. 8.
    W. Gong and D. M. Merfeld, Ann. Biomed. Eng., 28, 572–581 (2000).CrossRefGoogle Scholar
  9. 9.
    A. P. Bradshaw, J. Assoc. Res. Otolaryngol., 11, No. 2, 145–159 (2010).CrossRefGoogle Scholar
  10. 10.
    P. Selva, J. Morlier, and Y. Gourinat, Int. J. Comput. Vis Biomech., 3, No. 2, 149–156 (2010).Google Scholar
  11. 11.
    V. V. Alexandrov, T. B. Alexandrova, R. Vega, et al., in: Proc. 4th WSEAS Int. Conf. on Mathematical Biology and Ecology (MABE’ 08), Acapulco (2008).Google Scholar
  12. 12.
    C. F. Santos, J. Belinha, F. Gentil, et al., Acta Bioeng. Biomech., 19, No. 1, 3–15 (2017).Google Scholar
  13. 13.
    V. P. Demkin, P. P. Shchetinin, S. S. Melnichuk, et al., Russ. Phys. J., 60, No. 11, 2019–2024 (2017).CrossRefGoogle Scholar
  14. 14.
    A. L. Zuev, V. Yu. Mishlanov, A. I. Sudakov, et al., Ross. Zh. Biomekh., 18, No. 4, 491–497 (2012).Google Scholar
  15. 15.
    V. V. Alexandrov, A. Almanza, N. V. Kulikovskaya, et al., in: Mathematical Modeling of Complex Information Processing Systems, Moscow University Press (2001), pp. 26–41.Google Scholar
  16. 16.
    R. Van de Berg, N. Guinand, and T. A. K. Nguyen, Frontiers in System Neyrosciense, 8, 1–12 (2012); DOI:  https://doi.org/10.3389/fnsys.2014.00255.Google Scholar
  17. 17.
    W. Gong and D. M. Merfeld, Ann. Biomed. Eng., 28, 572–581 (2000).CrossRefGoogle Scholar
  18. 18.
    T. A. K. Nguyen, J. Digiovanna, S. Cavuscens, et al., J. Neural Eng., 13, No. 4, AN046023 (2016).ADSCrossRefGoogle Scholar
  19. 19.
    M. Handler, P. Schier, K. D. Fritscher, et al., Frontiers in Neyrosciense, 1, 1–12 (2017); DOI:  https://doi.org/10.3389/fnins.2017.00713.Google Scholar
  20. 20.
    I. S. Curthoys, C. H. Markham, and E. J. Curthoys, J. Morphology, 151, No. 1, 17–34 (1977).CrossRefGoogle Scholar
  21. 21.
    D. Das, F. A. Kamil, K. Biswas, and S. Dasa, RSC Adv., 35, 1–8 (2014); DOI:  https://doi.org/10.1039/c0xx00000x.Google Scholar
  22. 22.
    F. RattayI. C. Gebeshuber, and A. H. Gitter, J. Acoust. Soc. Am., 103, No. 3, 1558–1565 (1998).ADSCrossRefGoogle Scholar
  23. 23.
    R. Hayden, S. Sawyer, F. Frey, et al., Exp. Brain Res., 210 (3–4), 623–640 (2011).CrossRefGoogle Scholar
  24. 24.
    P. Marszalek, J. J. Zielinsky, M. Fikus, and T. Y. Tsong, Biophys. J., 59, 982–987 (1991).ADSCrossRefGoogle Scholar
  25. 25.
    D. Das, F. A. Kamil, K. Biswasa, and S. Dasa, RSC Adv., 4, 18178–18185 (2014).CrossRefGoogle Scholar
  26. 26.
    K. Wang, Y. Zhao, D. Chen, et al., Sci. Data, 4:170015, 1–8 (2017).Google Scholar
  27. 27.
    J.-H. Nam and R. Fettiplace, Plos One, 7, No. 11, 1–10 (2012).CrossRefGoogle Scholar
  28. 28.
    S. L. Johnson, eLIFE: Neuroscience, 4, 1–21 (2015).Google Scholar
  29. 29.
    R. W. Murray and W. T. W. Potts, Comp. Biochem. Physiol., 2, 65–76 (1961).CrossRefGoogle Scholar
  30. 30.
    E. Du, S. Ha, M. Diez-Silva, et al., Lab Chip., 13, 3903–3909 (2013).CrossRefGoogle Scholar
  31. 31.
    T. K. Bera, J. Med. Eng., 2014, 28 (2014).CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • V. P. Demkin
    • 1
    Email author
  • S. V. Melnichuk
    • 1
  • P. P. Shchetinin
    • 1
  • H. Kingma
    • 1
  • R. Van de Berg
    • 1
  1. 1.National Research Tomsk State UniversityTomskRussia

Personalised recommendations