Skip to main content
SpringerLink
Log in

Quantifying multidimensional control mechanisms of cardiovascular dynamics during multiple concurrent stressors

  • Original Article
  • Published:
Medical & Biological Engineering & Computing Aims and scope Submit manuscript

Abstract

Heartbeat regulation is achieved through different routes originating from central autonomic network sources, as well as peripheral control mechanisms. While previous studies successfully characterized cardiovascular regulatory mechanisms during a single stressor, to the best of our knowledge, a combination of multiple concurrent elicitations leading to the activation of different autonomic regulatory routes has not been investigated yet. Therefore, in this study, we propose a novel modeling framework for the quantification of heartbeat regulatory mechanisms driven by different neural routes. The framework is evaluated using two heartbeat datasets gathered from healthy subjects undergoing physical and mental stressors, as well as their concurrent administration. Experimental results indicate that more than 70% of the heartbeat regulatory dynamics is driven by the physical stressor when combining physical and cognitive/emotional stressors. The proposed framework provides quantitative insights and novel perspectives for neural activity on cardiac control dynamics, likely highlighting new biomarkers in the psychophysiology and physiopathology fields. A Matlab implementation of the proposed tool is available online.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Notes

  1. An implementation of the model can be found at https://github.com/shadishadi72/Disentangling-model-.git

References

  1. Hainsworth R (1995) The control and physiological importance of heart rate. Heart Rate Var 3–9

  2. Beissner F, Meissner K, Bär K-J, Napadow V (2013) The autonomic brain: an activation likelihood estimation meta-analysis for central processing of autonomic function. J Neurosci 33(25):10 503–10 511

    Article  CAS  Google Scholar 

  3. Wei L, Chen H, Wu G-R (2018) Heart rate variability associated with grey matter volumes in striatal and limbic structures of the central autonomic network. Brain Res 1681:14–20

    Article  CAS  Google Scholar 

  4. Benarroch EE (1993) The central autonomic network: functional organization, dysfunction, and perspective. Mayo Clin Proc 68(10):988–1001. Elsevier

    Article  CAS  Google Scholar 

  5. de la Cruz F, Schumann A, Köhler S, Reichenbach JR, Wagner G, Bär K-J (2019) The relationship between heart rate and functional connectivity of brain regions involved in autonomic control. NeuroImage

  6. Sklerov M, Dayan E, Browner N (2018) Functional neuroimaging of the central autonomic network: recent developments and clinical implications. Clin Auton Res 1–12

  7. Thayer JF, Åhs F, Fedrikson M, Sollers JJ III, Wager TD (2012) A meta-analysis of heart rate variability and neuroimaging studies: implications for heart rate variability as a marker of stress and health. Neurosci Biobehav Rev 36(2):747–756

    Article  Google Scholar 

  8. Thayer JF, Lane RD (2009) Claude bernard and the heart–brain connection: further elaboration of a model of neurovisceral integration. Neurosci Biobehav Rev 33(2):81–88

    Article  Google Scholar 

  9. La Rovere MT, et al. (2001) Baroreflex sensitivity and heart rate variability in the identification of patients at risk for life-threatening arrhythmias: implications for clinical trials. Circulation 103(16):2072–2077

    Article  CAS  Google Scholar 

  10. Dampney R, Coleman M, Fontes M, Hirooka Y, Horiuchi J, Li Y. -W., Polson J, Potts P, Tagawa T (2002) Central mechanisms underlying short-and long-term regulation of the cardiovascular system. Clin Exp Pharmacol Physiol 29(4):261–268

    Article  CAS  Google Scholar 

  11. Karemaker JM, Wesseling KH (2008) Variability in cardiovascular control: the baroreflex reconsidered. Cardiovasc Eng 8(1):23–29

    Article  Google Scholar 

  12. Charkoudian N, Rabbitts JA (2009) Sympathetic neural mechanisms in human cardiovascular health and disease. Mayo Clin Proc 84(9):822–830. Elsevier

    Article  Google Scholar 

  13. Barbieri R, Scilingo E, Valenza G (2017) Complexity and nonlinearity in cardiovascular signals. Springer, Berlin

    Book  Google Scholar 

  14. Hayano J, Yasuma F (2003) Hypothesis: respiratory sinus arrhythmia is an intrinsic resting function of cardiopulmonary system. Cardiovasc Res 58(1):1–9

    Article  CAS  Google Scholar 

  15. Grossman P, Taylor EW (2007) Toward understanding respiratory sinus arrhythmia: relations to cardiac vagal tone, evolution and biobehavioral functions. Biol Psychol 74(2):263–285

    Article  Google Scholar 

  16. Atterhög J, Eliasson K, Hjemdahl P (1981) Sympathoadrenal and cardiovascular responses to mental stress, isometric handgrip, and cold pressor test in asymptomatic young men with primary t wave abnormalities in the electrocardiogram. Br Heart J 46(3):311

    Article  Google Scholar 

  17. Peng R-C, Yan W-R, Zhou X-L, Zhang N-L, Lin W-H, Zhang Y-T (2015) Time-frequency analysis of heart rate variability during the cold pressor test using a time-varying autoregressive model. Physiol Meas 36(3):441

    Article  Google Scholar 

  18. Porta A, Tobaldini E, Guzzetti S, Furlan R, Montano N, Gnecchi-Ruscone T (2007) Assessment of cardiac autonomic modulation during graded head-up tilt by symbolic analysis of heart rate variability. Am J Physiol Heart Circ Physiol 293(1):H702–H708

    Article  CAS  Google Scholar 

  19. Posada-Quintero HF, Florian JP, Orjuela-Cañón ÁD, Chon KH (2016) Highly sensitive index of sympathetic activity based on time-frequency spectral analysis of electrodermal activity, American Journal of Physiology-Regulatory. Integr Comp Physiol 311(3):R582–R591

    Article  Google Scholar 

  20. Ghiasi S, Greco A, Barbieri R, Scilingo E, Valenza G (2020) Assessing autonomic function from electrodermal activity and heart rate variability during cold-pressor test and emotional challenge. Sci Rep 10(1):1–13

    Article  Google Scholar 

  21. McGinley JJ, Friedman BH (2015) Autonomic responses to lateralized cold pressor and facial cooling tasks. Psychophysiology 52(3):416–424

    Article  Google Scholar 

  22. Mourot L, Bouhaddi M, Regnard J (2009) Effects of the cold pressor test on cardiac autonomic control in normal subjects. Physiol Res 58(1):83

    Article  CAS  Google Scholar 

  23. Cui J, Wilson TE, Crandall CG (2002) Baroreflex modulation of muscle sympathetic nerve activity during cold pressor test in humans. Am J Physiol Heart Circ Physiol 282(5):H1717–H1723

    Article  CAS  Google Scholar 

  24. Bota PJ, Wang C, Fred AL, Da Silva HP (2019) A review, current challenges, and future possibilities on emotion recognition using machine learning and physiological signals. IEEE Access 7:140 990–141 020

    Article  Google Scholar 

  25. Aubert AE, et al. (2009) Effects of mental stress on autonomic cardiac modulation during weightlessness. Am J Physiol Heart Circ Physiol 298(1):H202–H209

    Article  Google Scholar 

  26. Javorka M, et al. (2017) Causal analysis of short-term cardiovascular variability: state-dependent contribution of feedback and feedforward mechanisms. Med Biol Eng Comput 55(2):179–190

    Article  Google Scholar 

  27. Russell JA (1980) A circumplex model of affect. J Pers Soc Psychol 39(6):1161

    Article  Google Scholar 

  28. Pan J, Tompkins WJ (1985) A real-time qrs detection algorithm. IEEE Trans Biomed Eng 3:230–236

    Article  Google Scholar 

  29. Valenza G, Greco A, Gentili C, Lanata A, Sebastiani L, Menicucci D, Gemignani A, Scilingo E (2016) Combining electroencephalographic activity and instantaneous heart rate for assessing brain–heart dynamics during visual emotional elicitation in healthy subjects, vol 374

  30. Barbieri R et al (2005) A point-process model of human heartbeat intervals: new definitions of heart rate and heart rate variability. Am J Physiol Heart Circ Physiol 288(1):H424–H435

    Article  CAS  Google Scholar 

  31. Ghiasi S, Greco A, Faes L, Javorka M, Barbieri R, Scilingo E, Valenza G (2019) Quantification of different regulatory pathways contributing to heartbeat dynamics during multiple stimuli: a proof of the concept. In: 2019 41st Annual international conference of the IEEE engineering in medicine and biology society (EMBC). IEEE, pp 4934–4937

  32. Porta A, Bassani T, Bari V, Pinna GD, Maestri R, Guzzetti S (2011) Accounting for respiration is necessary to reliably infer granger causality from cardiovascular variability series. IEEE Trans Biomed Eng 59(3):832–841

    Article  Google Scholar 

  33. Valenza G, Citi L, Saul JP, Barbieri R (2018) Measures of sympathetic and parasympathetic autonomic outflow from heartbeat dynamics. J Appl Physiol

Download references

Funding

This project has received partial funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No. 722022 “AffecTech”. Moreover, this work is also partially supported by the Italian Ministry of Education and Research (MIUR) in the framework of the CrossLab project (Departments of Excellence). MJ was supported by the grants APVV-0235-12, VEGA 1/0117/17, VEGA 1/0200/19 and VEGA 1/0200/20.

Author information

Authors and Affiliations

  1. Department of Information Engineering & Research Center E. Piaggio, University of Pisa, Pisa, Italy

    Shadi Ghiasi, Alberto Greco, Enzo Pasquale Scilingo & Gaetano Valenza

  2. Department of Engineering, University of Palermo, Palermo, Italy

    Luca Faes

  3. Department of Physiology and the Biomedical Center Martin, Jessenius Faculty of Medicine, Comenius University in Bratislava, Martin, Slovakia

    Michal Javorka

  4. Department of Electronics, Informatics and Bioengineering, Politecnico di Milano, Milano, Italy

    Riccardo Barbieri

Authors
  1. Shadi Ghiasi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alberto Greco
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Luca Faes
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Michal Javorka
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Riccardo Barbieri
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Enzo Pasquale Scilingo
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Gaetano Valenza
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shadi Ghiasi.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ghiasi, S., Greco, A., Faes, L. et al. Quantifying multidimensional control mechanisms of cardiovascular dynamics during multiple concurrent stressors. Med Biol Eng Comput 59, 775–785 (2021). https://doi.org/10.1007/s11517-020-02311-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-020-02311-9

Keywords

Search

Navigation