Skip to main content
SpringerLink
Log in

Effects of medical biofeedback trainings on acute stress by hybridizing heart rate variability and brain imaging

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

In this study, active and arousal elements of emotion associated with acute stress were systematically investigated with respect to the relations between the brain activity and autonomic nervous system. In this regard, we examined the differences in short-term heart rate variability (HRV) with respect to time-frequency domain characteristics, nonlinear features, and heart rhythm patterns, when breathing volitionally in a resonant frequency (RF) respiratory with International Affective Picture Systems (IAPS) triggered negative stimulus. In this regard, a sample-based event-related functional magnetic resonance imaging (efMRI) experiments were performed to verify the dynamic changes in brain lateralisation, and 105 healthy right-handed subjects participated in the HRV study while eight of them were randomly chosen to perform small sample based efMRI test. The experimental results suggest that when experiencing negative emotions, RF-based volitional breathing is sufficient to facilitate coherence of autonomic nervous system (ANS) performance, and shifted the brain activation toward left lateralized neural activity. In combination with the previous research on cerebral correlates of emotion, this study validated the feasibility of applying HRV biofeedback in the regulation of negative emotion in healthcare settings.

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

Similar content being viewed by others

References

  1. Demaree HA, Everhart DE (2004) Healthy high-hostiles: reduced parasympathetic activity and decreased sympathovagal flexibility during negative emotional processing. Personal Individ Differ 36:457–469

    Article  Google Scholar 

  2. Dong JG (2016) The role of heart rate variability in sports physiology. Exp Ther Med 11(5):1531–1536

    Article  Google Scholar 

  3. Gianaros PJ, Van der Veen FM, Jennings JR (2004) Regional cerebral blood flow correlates with heart period and high-frequency heart period variability during working-memory tasks: implications for the cortical and subcortical regulation of cardiac autonomic activity. Psychophysiology. 41(4):521–530

    Article  Google Scholar 

  4. Gross MJ, Shearer DA, Bringer JD, Hall R, Cook CJ, Kilduff LP (2016) Abbreviated resonant frequency training to augment heart rate variability and enhance on-demand emotional regulation in elite sport support staff. Appl Psychophysiol Biofeedback 41:263–274

    Article  Google Scholar 

  5. Han JW, Ji x, Hu XT, Zhu DJ, Li KM, Jiang X, Cui GB, Guo L, Liu TM (2013) Representing and retrieving video shots in human-centric brain imaging space. IEEE Trans Image Process 22(7):2723–2736

    Article  MathSciNet  Google Scholar 

  6. Han JW, Zhang DW, Hu XT, Guo L, Ren JC, Wu F (2015) Background prior-based salient object detection via deep reconstruction residual. IEEE Trans Circuits Syst Video Technol 25(8):1309–1321

    Article  Google Scholar 

  7. Han J et al (2015) Object detection in optical remote sensing images based on weakly supervised learning and high-level feature learning. IEEE Trans Geosci Remote Sens 53(6):3325–3337

    Article  Google Scholar 

  8. Jiménez Morgan S, Molina Mora JA (2017) Effect of heart rate variability biofeedback on sport performance, a systematic review. Appl Psychophysiol Biofeedback 42(2):1–11

    Google Scholar 

  9. Kim T, Pollock S, Lee D, O'Brien R, Keall P (2012) Audiovisual biofeedback improves diaphragm motion reproducibility in MRI. Med Phys 39(11):6921–6928

    Article  Google Scholar 

  10. Koenig J, Thayer JF (2016) Sex differences in healthy human heart rate variability: a meta-analysis. Neurosci Behav Rev 64:288–310

    Article  Google Scholar 

  11. Lehrer PM, Gevirtz R (2014) Heart rate variability biofeedback: how and why does it work? Front Psychol 5:756–765

    Article  Google Scholar 

  12. Lehrer PM, Vaschillo E, Vaschillo B (2000) Resonant frequency biofeedback training to increase cardiac variability: rationale and manual for training. Appl Psychophysiol Biofeedback 25(3):177–191

    Article  Google Scholar 

  13. Pirbhulal S, Shang P, Wu W, Sangaiah AK, Samuel OW, Li G (2018) Fuzzy vault-based biometric security method for tele-health monitoring systems. Comput Electr Eng 71:546–557

    Article  Google Scholar 

  14. Pirbhulal S, Zhang H, Mukhopadhyay SC, Wu W, Zhang Y-T (2018) Heart-beats based biometric random binary sequences generation to secure wireless body sensor networks. IEEE Trans Biomed Eng 65(12):2751–2759

    Article  Google Scholar 

  15. Prinsloo GE, Rauch HG, Derman WE (2014) A brief review and clinical application of heart rate variability biofeedback in sports, exercise, and rehabilitation medicine. Phys Sports Med 42(2):88–99

    Article  Google Scholar 

  16. Windthorst P, Mazurak N, Kuske M, Hipp A, Giel KE, Enck P et al (2017) Heart rate variability biofeedback therapy and graded exercise training in management of chronic fatigue syndrome: an exploratory pilot study. J Psychosom Res 3(1):6–13

    Article  Google Scholar 

  17. Wu W, Gil Y, Lee J (2012) Combination of wearable multi-biosensor platform and resonance frequency training for stress Management of the Unemployed Population. Sensors 12:13225–13248

    Article  Google Scholar 

  18. Wu W, Pirbhulal S, Zhang H, Mukhopadhyay SC (2018) Quantitative assessment for self-tracking of acute stress based on triangulation principle in a wearable sensor system. IEEE J Biomed Health Inf 23(2):703–713

    Article  Google Scholar 

  19. Wu W, Zhang H, Pirbhulal S, Mukhopadhyay SC, Zhang Y-T (2015) Assessment of biofeedback training for emotion management through wearable textile physiological monitoring system. IEEE Sensors J 15(1):7087–7095

    Article  Google Scholar 

  20. Xiao F, Xie X, Jiang Z, Sun L, Wang R (2016) Utility-aware data transmission scheme for delay tolerant networks. Peer-to-Peer Netw Appl 9(5):936–944

    Article  Google Scholar 

  21. Xiao F, Yang XK, Yang M, Sun LJ, Wang RC, Yang PL (2016) Surface coverage algorithm in directional sensor networks for three-dimensional complex terrains. Tsinghua Sci Technol 21(4):397–406

    Article  Google Scholar 

  22. Xiao X, Zheng W, Xia Y, Sun X, Peng Q, Guo Y A workload-aware VM consolidation method based on coalitional game for energy-saving in cloud, IEEE Access, to be published in 2019. doi: https://doi.org/10.1109/ACCESS.2019.2923464.

  23. Zhang H, Spinrad TL, Eisenberg N, Luo Y, Wang Z (2017) Young adults' internet addiction: prediction by the interaction of parental marital conflict and respiratory sinus arrhythmia. Int J Psychophysiol 17(1):148–156

    Article  Google Scholar 

  24. Zhang D et al (2017) Co-saliency detection via a self-paced multiple-instance learning framework. IEEE Trans Pattern Anal Mach Intell 39(5):865–878

    Article  Google Scholar 

  25. Zhu H, Xiao F, Sun LJ, Wang RC, Yang PL (2017) R-TTWD: robust device-free through-The-Wall detection of moving human with WiFi. IEEE J Sel Areas Commun 35(5):1090–1103

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported in part by This work was supported in part by the Shenzhen Governmental Basic Research Grant (JCYJ20170552198154152, JCYJ20160429174426094), the National Natural Science Foundation of China under grants (61873349, U180120019), the Guangdong Province Science and Technology Planning Project (2017B010125001), the Guangzhou Science and Technology Planning Project (201704020079).

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Xi’an University of Technology, Xi’an, China

    Xiaofan Wang & Shengjie Li

  2. School of Biomedical Enigneering, Sun Yat-sen University, Guangzhou, 510006, China

    Wanqing Wu

  3. Guangzhou, People’s Republic of China

    Wanqing Wu

Authors
  1. Xiaofan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shengjie Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wanqing Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wanqing Wu.

Ethics declarations

Conflicts of interest

The authors declare no conflict of interest.

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

Wang, X., Li, S. & Wu, W. Effects of medical biofeedback trainings on acute stress by hybridizing heart rate variability and brain imaging. Multimed Tools Appl 79, 10141–10155 (2020). https://doi.org/10.1007/s11042-019-08004-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-019-08004-2

Keywords

Search

Navigation