Skip to main content

Advertisement

Log in

A Method for More Accurate Determination of Resonance Frequency of the Cardiovascular System, and Evaluation of a Program to Perform It

  • Published:
Applied Psychophysiology and Biofeedback Aims and scope Submit manuscript

Abstract

This study validated a more exact automated method of determining cardiovascular resonance frequency (RF) against the “stepped” protocol described by Lehrer et al. (Appl Psychophysiol Biofeedback 25(3):177–191, https://doi.org/10.1023/a:1009554825745, 2000; in Foundations of heart rate variability biofeedback: A book of readings, The Association for Applied Psychophysiology and Biofeedback, pp 9–19, 2016). Thirteen participants completed a 15-min RF determination session by each method. The “stepped” protocol assesses HRV in five 3-min stationary windows from 4.5 to 6.5 breaths per minute (bpm), decreasing in 0.5 bpm steps. Multiple criteria, subjectively weighted by the clinician, determines RF. For this study, the proposed method used a sliding window with a fixed rate of change (67.04 ms per breath) at each of 78 breath cycles ranging from 4.25 to 6.75 bpm. Its algorithm analyzes IBI to locate the midpoint of the 1-min region of stable maximum peak-trough variability. RF is quantified from breath duration at that point. The software generates a visual display of superimposed HR and breathing data. Thus, the new method fully automates RF determination. Eleven of the 13 matched pairs fell within the 0.5 bpm resolution of the stepped method. Comparisons of LF power generated by the autoregressive (AR) spectral method showed a strong correlation in LF power production by the stepped and sliding methods (R = 0.751, p = 0.000). The “sliding” pacing protocol was favored by 69% of participants (p < 0.02). The new, fully-automated, method may facilitate both in-person and remote HRV biofeedback training. Software is available open-source.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2

Similar content being viewed by others

References

  • Aubert, A. E., & Verheyden, B. (2016). Neurocardiology: A bridge between the brain and the heart. In D. Moss & F. Shaffer (Eds.), Foundations of heart rate variability Biofeedback: A book of readings (pp. 42–45). The Association for Applied Psychophysiology and Biofeedback. ISBN 978-0-9842979-5-5.

  • Berntson, G. G., Cacioppo, J. T., & Quigley, K. S. (1993). Respiratory sinus arrhythmia: Autonomic origins, physiological mechanisms, and psychophysiological implications. Psychophysiology, 30(2), 183–196. https://doi.org/10.1111/j.1469-8986.1993.tb01731.x

    Article  PubMed  Google Scholar 

  • ChuDuc, H., NguyenPhan, K., & NguyenViet, D. (2013). A review of heart rate variability and its applications. APCBEE Procedia, 7, 80–85. https://doi.org/10.1016/j.apcbee.2013.08.016

    Article  Google Scholar 

  • Diehl, R. R., Linden, D., Lucke, D., & Berlit, P. (1995). Phase relationship between cerebral blood flow velocity and blood pressure: A clinical test of autoregulation. Stroke, 26(10), 1801–1804.

    Article  PubMed  Google Scholar 

  • Ellis, P. D. (2009). Thresholds for interpreting effect sizes (Web Page). http://www.polyu.edu.hk/mm/effectsizefaqs/thresholds_for_interpreting_effect_sizes2.html

  • Ernst, G. (2017). Heart-rate variability-more than heart beats? Frontiers in Public Health, 5, 240. https://doi.org/10.3389/fpubh.2017.00240

    Article  PubMed  PubMed Central  Google Scholar 

  • Ezure, K. (2001). Information processing at the nucleus tractus solitarii and respiratory rhythm generation in neural control of breathing (meeting abstract). Respiratory Research, 2(1), S5–S6.

    Google Scholar 

  • Ezure, K. (2004). Reflections on respiratory rhythm generation. In Progress in brain research (Vol. 143, pp. 67–74). Elsevier. https://doi.org/10.1016/S0079-6123(03)43007-0

  • Gevirtz, R. (2016). The promise of heart rate variability biofeedback: Evidence-based applications. In D. Moss & F. Shaffer (Eds.), Foundations of heart rate variability biofeedback: A book of readings (pp. 20–26). The Association for Applied Psychophysiology and Biofeedback. ISBN 978-0-9842979-5-5.

  • Glen, S. (2015). Autoregressive model: Definition & the AR process. StatisticsHowTo.com. Elementary Statistics for the rest of us! Retrieved from https://www.statisticshowto.com/autoregressive-model

  • Goessl, V. C., Curtiss, J. E., & Hofmann, S. G. (2017). The effect of heart rate variability biofeedback training on stress and anxiety: A meta-analysis. Psychological Medicine, 47(15), 2578–2586.

    Article  PubMed  Google Scholar 

  • Grodins, F. S. (1963). Control theory and biological systems. Columbia University Press.

    Google Scholar 

  • Hayano, J., Yasuma, F., Okada, A., Mukai, S., & Fujinami, T. (1996). Respiratory sinus arrhythmia: A phenomenon improving pulmonary gas exchange and circulatory efficiency. Circulation, 94(4), 842–847. https://doi.org/10.1161/01.CIR.94.4.842

    Article  PubMed  Google Scholar 

  • Henderson, L. A., Richard, C. A., Macey, P. M., Runquist, M. L., Yu, P. L., Galons, J.-P., & Harper, R. M. (2004). Functional magnetic resonance signal changes in neural structures to baroreceptor reflex activation. Journal of Applied Physiology, 96(2), 693–703. https://doi.org/10.1152/japplphysiol.00852.2003

    Article  PubMed  Google Scholar 

  • Hoffstaedter, F., Grefkes, C., Caspers, S., Roski, C., Palomero-Gallagher, N., Laird, A. R., Fox, P. T., & Eickhoff, S. B. (2014). The role of anterior midcingulate cortex in cognitive motor control: Evidence from functional connectivity analyses. Human Brain Mapping, 35(6), 2741–2753. https://doi.org/10.1002/hbm.22363

    Article  PubMed  Google Scholar 

  • Lehrer, P., & Eddie, D. (2013). Dynamic processes in regulation and some implications for biofeedback and biobehavioral interventions. Applied Psychophysiology and Biofeedback, 38(2), 143–155. https://doi.org/10.1007/s10484-013-9217-6

    Article  PubMed  PubMed Central  Google Scholar 

  • Lehrer, P., & Kaur, K. (2019). Training in slow (6/min) breathing. Paper presented at the annual meeting of the American Psychosomatic Society, Vancouver, BC, Canada (submitted for publication).

  • Lehrer, P., Kaur, K., Sharma, A., Shah, K., Huseby, R., Bhavsar, J., & Zhang, Y. (2020). Heart rate variability biofeedback improves emotional and physical health and performance: A systematic review and meta-analysis. Applied Psychophysiology and Biofeedback, 45(3), 109–129. https://doi.org/10.1007/s10484-020-09466-z

    Article  PubMed  Google Scholar 

  • Lehrer, P., Vaschillo, B., Zucker, T., Graves, J., Katsamanis, M., Aviles, M., & Wamboldt, F. (2016). Protocol for heart rate variability biofeedback training. In D. Moss & F. Shaffer (Eds.), Foundations of heart rate variability biofeedback: A book of readings (pp. 9–19). The Association for Applied Psychophysiology and Biofeedback. ISBN 978-0-9842979-5-5.

  • Lehrer, P. M., Vaschillo, E. G., & Vidali, V. (2020). Heart rate and breathing are not always in phase during resonance frequency breathing. Applied Psychophysiology and Biofeedback, 45(3), 145–152. https://doi.org/10.1007/s10484-020-09459-y

    Article  PubMed  Google Scholar 

  • Lehrer, P. M., Vaschillo, E., & Vaschillo, B. (2000). Resonant frequency biofeedback training to increase cardiac variability: Rationale and manual for training. Applied Psychophysiology and Biofeedback, 25(3), 177–191. https://doi.org/10.1023/a:1009554825745

    Article  PubMed  Google Scholar 

  • Lehrer, P. M., & Vaschillo, E. (2016). The future of heart rate variability. In D. Moss & F. Shaffer (Eds.), Foundations of heart rate variability biofeedback: A book of readings (pp. 27–30). The Association for Applied Psychophysiology and Biofeedback. ISBN 978-0-9842979-5-5.

  • Mather, M., & Thayer, J. F. (2018). How heart rate variability affects emotion regulation brain networks. Current Opinion in Behavioral Sciences, 19, 98–104. https://doi.org/10.1016/j.cobeha.2017.12.017

    Article  PubMed  PubMed Central  Google Scholar 

  • Pfurtscheller, G., Schwerdtfeger, A. R., Seither-Preisler, A., Brunner, C., Stefan Aigner, C., Brito, J., Carmo, M. P., & Andrade, A. (2017). Brain–heart communication: Evidence for “central pacemaker” oscillations with a dominant frequency at 0.1 Hz in the cingulum. Clinical Neurophysiology, 128(1), 183–193. https://doi.org/10.1016/j.clinph.2016.10.097

    Article  PubMed  Google Scholar 

  • Pigolotti, S., Krishna, S., & Jensen, M. H. (2007). Oscillation patterns in negative feedback loops. Proceedings of the National Academy of Sciences of the United States of America, 104(16), 6533–6537. https://doi.org/10.1073/pnas.0610759104

    Article  PubMed  PubMed Central  Google Scholar 

  • Ringwood, J. V., & Malpas, S. C. (2001). Slow oscillations in blood pressure via a nonlinear feedback model. American Journal of Physiology-Regulatory, Integrative and Comparative Physiology, 280(4), R1105–R1115. https://doi.org/10.1152/ajpregu.2001.280.4.R1105

    Article  PubMed  Google Scholar 

  • Ritz, T., & Dahme, B. (2006). Implementation and Interpretation of respiratory sinus arrhythmia measures in psychosomatic medicine: Practice against better evidence? Psychosomatic Medicine, 68(4), 617–627.

    Article  PubMed  Google Scholar 

  • Shaffer, F., McCraty, R., & Zerr, C. L. (2014). A healthy heart is not a metronome: An integrative review of the heart’s anatomy and heart rate variability. Frontiers in Psychology. https://doi.org/10.3389/fpsyg.2014.01040

    Article  PubMed  PubMed Central  Google Scholar 

  • Sugimoto, Y., Kiyono, K., & Yoshino, K. (2019). Analysis of the relationship between amplitude modulation of low frequency heart rate variability and blood pressure variability. Advanced Biomedical Engineering, 8, 78–84. https://doi.org/10.14326/abc.8.78

    Article  Google Scholar 

  • Tarvainen, M. P., Niskanen, J. P., Lipponen, J. A., Ranta-aho, P. O., Karjalainen, P. A. (2009). Kubios HRV—A software for advanced heart rate variability analysis. In J. Vander Sloten, P. Verdonck, M. Nyssen, & J. Haueisen (Eds.), 4th European conference of the international federation for medical and biological engineering. IFMBE proceedings. https://doi.org/10.1007/978-3-540-89208-3_243

  • Task Force. (1996). Heart rate variability: Standards of measurement, physiological interpretation, and clinical use. Circulation, 93(5), 1043–1065. https://doi.org/10.1161/01.CIR.93.5.1043

    Article  Google Scholar 

  • Vaschillo, E., Vaschillo, B., Bates, M. E., Lehrer, P., France, C. H., & Trost, Z. (2007). Rhythmical muscle tension mimics heart rate variability biofeedback. Applied Psychophysiology and Biofeedback, 32(2), 132–133.

    Google Scholar 

  • Vaschillo, E., Lehrer, P., Rishe, N., & Konstantinov, M. (2002). Heart rate variability biofeedback as a method for assessing baroreflex function: A preliminary study of resonance in the cardiovascular system. Applied Psychophysiology and Biofeedback, 27(1), 1–27. https://doi.org/10.1023/A:1014587304314

    Article  PubMed  Google Scholar 

  • Vaschillo, E. G., Bates, M. E., Vaschillo, B., Lehrer, P., Udo, T., Mun, E. Y., & Ray, S. (2008). Heart rate variability response to alcohol, placebo, and emotional picture cue challenges: Effects of 0.1-Hz stimulation. Psychophysiology, 45(5), 847–858. https://doi.org/10.1111/j.1469-8986.2008.00673.x

    Article  PubMed  PubMed Central  Google Scholar 

  • Vaschillo, E. G., Vaschillo, B., & Lehrer, P. M. (2006). Characteristics of resonance in heart rate variability stimulated by biofeedback. Applied Psychophysiology and Biofeedback, 31(2), 129–142. https://doi.org/10.1007/s10484-006-9009-3

    Article  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

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

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 1791 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fisher, L.R., Lehrer, P.M. A Method for More Accurate Determination of Resonance Frequency of the Cardiovascular System, and Evaluation of a Program to Perform It. Appl Psychophysiol Biofeedback 47, 17–26 (2022). https://doi.org/10.1007/s10484-021-09524-0

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10484-021-09524-0

Keywords

Navigation