On the relationship between ultraslow electroencephalogram oscillations and heart rate variability in vascular pathology
DOI:
https://doi.org/10.33910/2687-1270-2025-6-1-66-74Keywords:
vascular pathology, electroencephalogram, electrocardiogram, frequency dynamics, wavelet transformAbstract
This study investigates the dynamic relationship between ultraslow electroencephalogram (EEG) oscillations and heart rate variability (HRV) in healthy individuals and patients with vascular pathologies, including vegetative-vascular dystonia and vertebrobasilar insufficiency. Using synchrosqueezed wavelet transform analysis — a method particularly effective for extracting instantaneous frequencies from noisy physiological data — we compared bioelectrical activity patterns between control subjects and clinical groups. The focus on ultraslow oscillations (<0.5 Hz) stems from their established role in cardiovascular regulation, unlike higher EEG frequencies. Key findings demonstrate pathological variations in synchronization dynamics: patients with vertebrobasilar insufficiency exhibited significantly shorter synchronization latencies between EEG and HRV frequencies compared to vegetative-vascular dystonia cases.
References
Daubechies, I. (1992) Ten lectures on wavelets. Philadelphia: SIAM Publ., 357 p. (CMBS-NSF Regional conference series in applied mathematics. No. 61). https://doi.org/10.1137/1.9781611970104 (In English)
Daubechies, I., Lu, J., Wu, H.-T. (2011) Synchrosqueezed wavelet transforms: An empirical mode decomposition-like tool. Applied and Computational Harmonic Analysis, vol. 30, no. 2, pp. 243–261. https://doi.org/10.1016/j.acha.2010.08.002 (In English)
Dick, O. E., Glazov, A. L. (2021) Estimation of the synchronization between intermittent photic stimulation and brain response in hypertension disease by the recurrence and synchrosqueezed wavelet transform. Neurocomputing, vol. 455, pp. 163–177. https://doi.org/10.1016/j.neucom.2021.05.038 (In English)
Dick, O. E., Glazov, A. L. (2023) Revealing the coupling directionality and synchronization between time series from physiological data by analysis of joint recurrences. Chaos, Solitons & Fractals, vol. 173, article 113768. https://doi.org/10.1016/j.chaos.2023.113768 (In English)
Dick, O. E., Lyubashina, O. A. (2024) Comparative pairwise analysis of the relationships between physiological rhythms using synchrosqueezed wavelet transform, phase dynamics modelling and recurrence. Nonlinear Dynamics, vol. 112, no. 22, pp. 19825–19848. https://doi.org/10.1007/s11071-024-10089-w (In English)
Dick, T. E., Hsieh, Y.-H., Dhingra, R. R. et al. (2014) Cardiorespiratory сoupling: Сommon rhythms in cardiac, sympathetic, and respiratory activities. Progress in Brain Research, vol. 209, pp. 191–205. https://doi.org/10.1016/B978-0-444-63274-6.00010-2 (In English)
Dik, O. E., Glazov, A. L. (2021) Parametry fazovoj sinkhronizatsii v elektroentsefalograficheskikh patternakh kak markery kognitivnykh narushenij [Parameters of phase synchronization in electroencephalographic patterns as markers of cognitive impairment]. Zhurnal tekhnicheskoj fiziki — Technical Physics, vol. 66, no. 4, pp. 560–570. https://doi.org/10.1134/S1063784221040058 (In English)
Iadecola, C., Gottesman, R. F. (2019) Neurovascular and cognitive dysfunction in hypertension: Epidemiology, pathobiology, and treatment. Circulation Research, vol. 124, no. 7, pp. 1025–1044. https://doi.org/10.1161/CIRCRESAHA.118.313260 (In English)
Hestad, K., Engedal, K., Schirmer, H., Strand, B. H. (2020) The effect of blood pressure on cognitive performance. An 8-year follow-up of the Tromsø study, comprising people aged 45–74 years. Frontiers in Psychology, vol. 11, article 607. https://doi.org/10.3389/fpsyg.2020.00607 (In English)
Knyazev, G. G. (2012) EEG delta oscillations as a correlate of basic homeostatic and motivational processes. Neuroscience & Biobehavioral Reviews, vol. 36, no. 1, pp. 677–695. https://doi.org/10.1016/j.neubiorev.2011.10.002 (In English)
Limberg, J. K., Morgan, B. J., Schrage, W. G., Dempsey, J. A. (2013) Respiratory influences on muscle sympathetic nerve activity and vascular conductance in the steady state. American Journal of Physiology — Heart and Circulatory Physiology, vol. 304, no. 12, pp. H1615–H1623. https://doi.org/10.1152/ajpheart.00112.2013 (In English)
Mills, K. T. (2016) Global disparities of hypertension prevalence and control: A systematic analysis of population-based studies from 90 countries. Circulation, vol. 134, no. 6, pp. 441–450. https://doi.org/10.1161/CIRCULATIONAHA.115.018912 (In English)
Pikovsky, A. S., Rosenblum, M. G., Kurths, J. (2011) Synchronization: A universal concept in nonlinear sciences. Cambridge: Cambridge University Press, 411 p. (In English)
Tadic, M., Cuspidi, C., Hering, D. (2016) Hypertension and cognitive dysfunction in elderly: Blood pressure management for this global burden. BMC Cardiovascular Disorders, vol. 16, no. 1, article 208. https://doi.org/10.1186/s12872-016-0386-0 (In English)
Ungvari, Z., Toth, P., Tarantini, S. et al. (2021) Hypertension-induced cognitive impairment: From pathophysiology to public health. Nature Reviews Nephrology, vol. 17, no. 10, pp. 639–654. https://doi.org/10.1038/s41581-021-00430-6 (In English)
Walker, K. A., Sharrett, A. R., Wu, A. et al. (2019) Association of midlife to late-life blood pressure patterns with incident dementia. Journal of the American Medical Association, vol. 322, no. 6, pp. 535–545. https://doi.org/10.1001/jama.2019.10575 (In English)
Wu, H.-T., Chan, Y.-H., Lin, Y.-T. et al. (2014) Using synchrosqueezing transform to discover breathing dynamics from ECG signals. Applied and Computational Harmonic Analysis, vol. 36, no. 2, pp. 354–359. https://doi.org/10.1016/j.acha.2013.07.003 (In English)
Wu, H.-T., Lewis, G. F., Davila, M. I. et al. (2016) Optimizing estimates of instantaneous heart rate from pulse wave signals with the synchrosqueezing transform. Methods of Information in Medicine, vol. 55, no. 5, pp. 463–472. https://doi.org/10.3414/ME16-01-0026 (In English)
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Olga E. Dick
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
The work is provided under the terms of the Public Offer and of Creative Commons public license Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). This license allows an unlimited number of persons to reproduce and share the Licensed Material in all media and formats. Any use of the Licensed Material shall contain an identification of its Creator(s) and must be for non-commercial purposes only.
