Literature DB >> 34655366

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

Lorrie R Fisher1, Paul M Lehrer2.   

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.
© 2021. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Entities:  

Keywords:  Breath pacing; Exact resonance frequency; HRV resonance frequency software; Heart rate resonance frequency protocol; Peak trough heart rate variability

Mesh:

Year:  2021        PMID: 34655366     DOI: 10.1007/s10484-021-09524-0

Source DB:  PubMed          Journal:  Appl Psychophysiol Biofeedback        ISSN: 1090-0586


  17 in total

1.  Resonant frequency biofeedback training to increase cardiac variability: rationale and manual for training.

Authors:  P M Lehrer; E Vaschillo; B Vaschillo
Journal:  Appl Psychophysiol Biofeedback       Date:  2000-09

2.  Functional magnetic resonance signal changes in neural structures to baroreceptor reflex activation.

Authors:  Luke A Henderson; Chris A Richard; Paul M Macey; Matthew L Runquist; Pearl L Yu; Jean-Philippe Galons; Ronald M Harper
Journal:  J Appl Physiol (1985)       Date:  2003-10-17

3.  Respiratory sinus arrhythmia. A phenomenon improving pulmonary gas exchange and circulatory efficiency.

Authors:  J Hayano; F Yasuma; A Okada; S Mukai; T Fujinami
Journal:  Circulation       Date:  1996-08-15       Impact factor: 29.690

4.  The role of anterior midcingulate cortex in cognitive motor control: evidence from functional connectivity analyses.

Authors:  Felix Hoffstaedter; Christian Grefkes; Svenja Caspers; Christian Roski; Nicola Palomero-Gallagher; Angie R Laird; Peter T Fox; Simon B Eickhoff
Journal:  Hum Brain Mapp       Date:  2013-09-24       Impact factor: 5.038

5.  Heart Rate Variability Biofeedback Improves Emotional and Physical Health and Performance: A Systematic Review and Meta Analysis.

Authors:  Paul Lehrer; Karenjot Kaur; Agratta Sharma; Khushbu Shah; Robert Huseby; Jay Bhavsar; Yingting Zhang
Journal:  Appl Psychophysiol Biofeedback       Date:  2020-09

Review 6.  Respiratory sinus arrhythmia: autonomic origins, physiological mechanisms, and psychophysiological implications.

Authors:  G G Berntson; J T Cacioppo; K S Quigley
Journal:  Psychophysiology       Date:  1993-03       Impact factor: 4.016

7.  Heart Rate and Breathing Are Not Always in Phase During Resonance Frequency Breathing.

Authors:  Paul M Lehrer; Evgeny G Vaschillo; Vinay Vidali
Journal:  Appl Psychophysiol Biofeedback       Date:  2020-09

8.  How heart rate variability affects emotion regulation brain networks.

Authors:  Mara Mather; Julian Thayer
Journal:  Curr Opin Behav Sci       Date:  2018-02

Review 9.  Dynamic processes in regulation and some implications for biofeedback and biobehavioral interventions.

Authors:  Paul Lehrer; David Eddie
Journal:  Appl Psychophysiol Biofeedback       Date:  2013-06

Review 10.  Heart-Rate Variability-More than Heart Beats?

Authors:  Gernot Ernst
Journal:  Front Public Health       Date:  2017-09-11
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  1 in total

1.  Dynamic Phase Extraction: Applications in Pulse Rate Variability.

Authors:  Christopher H Li; Franklin S Ly; Kegan Woodhouse; John Chen; Zhuowei Cheng; Tyler Santander; Nirmit Ashar; Elyes Turki; Henry T Yang; Michael Miller; Linda Petzold; Paul K Hansma
Journal:  Appl Psychophysiol Biofeedback       Date:  2022-06-15
  1 in total

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