, Volume 46, Issue 2, pp 139–148 | Cite as

Correlations between Indices of the Heart Rate Variability and Parameters of Ongoing EEG in Patients Suffering from Chronic Renal Pathology

  • І. L. PopovychEmail author
  • O. V. Kozyavkіna
  • N. V. Kozyavkіna
  • T. A. Korolyshyn
  • Yu. S. Lukovych
  • L. G. Barylyak

We examined correlations between indices of the heart rate variability (HRV) measured according to the cardiointervalogram (CIG) and parameters of the main rhythms of EEG recorded in a parallel mode in men suffering from chronic pyelonephritis (in the remission phase). We obtained a few equations of multiple regression that reflect interrelations between the absolute and relative (normalized) spectral and time-domain indices of CIG, on the one hand, and the amplitude/frequency and spectral parameters of EEG, on the other hand. According to values of the coefficient of canonical correlation with EEG parameters, CIG indices formed the following sequence: amplitude of the mode (R = 0.72), spectral power density (SPD) of the low-frequency (LF) component (R = 0.66), pNN50 (R = 0.65), mode (R = 0.64), SDNN (R = 0.63), SPD of the very low-frequency (VLF) component (R = 0.625), SPD of the high-frequency (HF) component (R = 0.55), RMSSD (R = 0.545), SPD of the ultralow-frequency (ULF) component (R = 0.455), and variation range (R = 0.38). Relative SPDs of the CIG components, similarly to LFnu, showed weaker correlations with EEG parameters (R = 0.535-0.42 and R = 0.42), while the ratio between the LF and HF powers demonstrated comparatively strong correlation (R = 0.56). The Baevsky stress index showed maximum correlation with EEG parameters (R = 0.80). According to the factor loadings, the absolute SPD of the α rhythm in leads O1 (r * = -0.51), Р3 (r* = -0.35) і С4 (r* = -0.29), that of the δ rhythm in leads Fp1 (r* = -0.41), O1 (r* = -0.40), T5 (r* = -0,39), F7 (r* = -0.39), and T6 (r* = -0.35), that of the β rhythm in locus О1 (r* = -0.34), relative SPD of the θ rhythm in loci F8 (r* = 0.38), Fр1 (r* = 0.32), and F4 (r* = 0.28), index of the θ rhythm (r* = 0.47), and also the modal β rhythm frequency (r * = 0.43) exert most significant influences on variability of the heart rhythm.


heart rate variability (HRV) cardiointervalogram (CIG) EEG autonomic regulation of the functions interrelations between the CIG and EEG parameters canonical correlation regression models chronic renal pathologies 


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  1. 1.
    “Heart rate variability. Standards of measurement, physiological interpretation, and clinical use. Task force of ESC and NASPE,” Circulation, 93, No. 5, 1043-1065 (1996).Google Scholar
  2. 2.
    S. A. Kotel’nikov, A. D. Nozdrachev, M. M. Odinak, et al. “Heart rate variability: Concepts on the mechanisms,” Fiziol. Chel., 28, No. 1, 130-140 (2002).Google Scholar
  3. 3.
    O. V. Korkushko, A. V. Pisaruk, and V. B. Shatilo “Importance of analysis of the heart rate variability in cardiology: Age-related aspects,” Krovoobig Hemostaz, No. 1/2, 127-139 (2009).Google Scholar
  4. 4.
    D. Tolkunov, D. Rubin, and L. R. Mujica-Parodi, “Power spectrum scale invariance quantifies limbic dysregulation in trait anxious adults using fMRI: adapting methods optimized for characterizing autonomic dysregulation to neural dynamic timeseries,” Neuroimage, 50, No. 1, 72 (2010).PubMedCentralPubMedCrossRefGoogle Scholar
  5. 5.
    M. Balle, X. Bornas, M. Tortella-Feliu, et al., “Resting parietal EEG asymmetry and cardiac vagal tone predict attentional control,” Biol. Psychol., 93(2), 257-261 (2013).PubMedCrossRefGoogle Scholar
  6. 6.
    B. R. Cahn and J. Polish, “Meditation states and traits: EEG, ERP and neuroimaging studies,” Psychol. Bull., 132, 180-211 (2006).PubMedCrossRefGoogle Scholar
  7. 7.
    F. Jurysta, P. Van de Borne, P.-F. Migeotte, et al., “A study of the dynamic interactions between sleep EEG and heart rate variability in healthy young men,” Clin. Neurophysiol., 114, No. 11, 2146-2155 (2003).PubMedCrossRefGoogle Scholar
  8. 8.
    F. Jurysta, P. Van de Borne, J.-P. Lanquart, et al., “Progressive aging does not alter the interactions between autonomic cardiac activity and delta EEG power,” Clin. Neurophysiol., 116, 871-877 (2005).PubMedCrossRefGoogle Scholar
  9. 9.
    S. C. Matthews, M. P. Paulus, A. N. Simmons, et al., “Functional subdivision with anterior cingulate cortex and their relationship to autonomic nervous system function,” Neuroimage, 22, No. 3, 1151-1156 (2004).PubMedCrossRefGoogle Scholar
  10. 10.
    H. D. Critchley, “Neural mechanisms of autonomic, affective, and cognitive integration,” J. Comp. Neurol., 493, 154-166 (2005).PubMedCrossRefGoogle Scholar
  11. 11.
    H. D. Critchley, “The human cortex responds to an interoceptive challenge,” Proc. Natl. Acad. Sci. USA, 101, 6333-6334 (2004).PubMedCentralPubMedCrossRefGoogle Scholar
  12. 12.
    Y. Ohtake, T. Hamada, T. Murata, et al., “The assotiation between autonomic response status and the changes in EEG activity during mental arithmetic task,” Rinsho Byori, 55, No. 12, 1075-1079 (2007).PubMedGoogle Scholar
  13. 13.
    S. M. Oppenheimer, G. Kedem, and W. M. Martin, “Left insular cortex lesions perturb cardiac autonomic tone in humans,” Clin. Auton. Res., 6, 131-140 (1996).PubMedCrossRefGoogle Scholar
  14. 14.
    G. E. Prinsloo, H. G. Rauch, D. Karpul, and W. E. Derman, “The effect of a single session of short duration heart rate variability biofeedback on EEG: a pilot study,” Appl. Psychophysiol. Biofeedback, 38, No. 1, 45-56 (2013).PubMedCrossRefGoogle Scholar
  15. 15.
    A. R. Subhani, X. Likun, and A. Saeed Malik, “Association of autonomic nervous system and EEG scalp potential during playing 2D Grand Turismo 5,” Conf. Proc. IEEE Eng. Med. Biol. Soc., 3420-3423 (2012).Google Scholar
  16. 16.
    S. Tiinanen, A. Määttä, M. Silverhuth, et al., “HRV and EEG based indicators of stress in children with Asperger syndrome in audio-visual stimulus test,” Conf. Proc. IEEE Eng. Med. Biol. Soc., 2021-2024 (2011).Google Scholar
  17. 17.
    H. Wahbeh and B. S. Oken, “Peak high-frequency HRV and peak alpha frequency is higher in PTSD,” Appl. Psychophysiol. Biofeedback, 38, No. 1, 57-69 (2013).PubMedCentralPubMedCrossRefGoogle Scholar
  18. 18.
    Yi-Y. Tang, Y. Ma, Ya. Fan, et al., “Central and autonomic nervous system interaction is altered by short-term meditation,” Proc. Natl. Acad. Sci. USA, 106, No. 22, 8865-8870 (2009).PubMedCentralPubMedCrossRefGoogle Scholar
  19. 19.
    I. L. Popovych, Yu. S. Lukovych, T. A. Korolyshyn, et al., “Relationship between the parameters of heart rate variability and background EEG activity in healthy men,” J. Health Sci., 3, No. 4, 217-240 (2013).Google Scholar
  20. 20.
    R. M. Baevsky and G. G. Ivanov, “Heart rate variability: Theoretical aspects and possibilities for clinical using,” Ultrazvuk. Funktsion. Diagnost., No. 3, 106-127 (2001).Google Scholar
  21. 21.
    H. Noguchi, T. Sakaguchi, and M. Sato, “Physiological effects of sudden change in illuminance during dark-adapted state,” Appl. Human Sci., 18, No. 3, 109-114 (1999).PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • І. L. Popovych
    • 1
    Email author
  • O. V. Kozyavkіna
    • 2
  • N. V. Kozyavkіna
    • 2
  • T. A. Korolyshyn
    • 1
  • Yu. S. Lukovych
    • 3
  • L. G. Barylyak
    • 1
  1. 1.Bogomolets Institute of Physiology of the National Academy of Sciences of UkraineKyivUkraine
  2. 2.Ukrainian Research Institute of Transport MedicineMinistry of Public Health of UkraineOdessaUkraine
  3. 3.“Medpalace” Diagnostic CenterTruskavetsUkraine

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