Abstract
Since the seminal studies by Sayers (Ergonomics 16:17–32, 1973) and Akselrod et al. (Science 213:220–222, 1981) a few decades ago, it became clear that beat-by-beat oscillations in RR interval length (i.e. heart-rate variability [HRV]) contain information on underlying neural-control mechanisms based on the instantaneous balance between parasympathetic and sympathetic innervation. Over the years, the number of studies addressing HRV has increased markedly and now outnumbers 23,000. Despite such a large interest, there is still a continuing debate about interpretation of indices produced by computer analysis of HRV.
The main part of studies relies on spectral techniques to extract parameters that are linked to hidden information. The general idea is that these proxies of autonomic regulation can be useful to clinical applications in various conditions in which autonomic dysregulation may play a role. There are, however, serious shortcomings related to algorithms, interpretation, and the hidden value of individual indices. In particular, it appears that specific training is necessary to interpret the hidden informational value of HRV. This technical complexity represents a severe barrier to large-scale clinical applications. Moreover, important differences in HRV separate the sexes, and age plays an additional confounding role.
We present here a preliminary application of a novel unitary index of RR variability (Autonomic Nervous System Index of cardiac regulation) capable of providing information on the performance of autonomic regulation using a percentile rank position as projected on a large benchmark population. A summary of the underlying sympatho-vagal model is also presented.
Heart rate variability. Illustration by Piet Michiels, Leuven, Belgium
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Sacks O. The man who mistook his wife for a hat. New York: Summit Books; 1985.
Editorial. The soft science of medicine. Lancet. 2004;363(9417):1247.
Hess WR. Nobel Lecture: The Central Control of the Activity of “Internal Organs”. Nobelprize.org.Nobel Media AB 2014. Web. 2016. http://www.nobelprize.org/nobel_prizes/medicine/laureates/1949/hess-lecture.html
Langley JN. The autonomic nervous system (Pt. I). Cambridge: W. Heffer & Sons; 1921.
Burke RE. Sir Charles Sherrington’s the integrative action of the nervous system: a centenary appreciation. Brain. 2007;130(4):887–94.
Malliani A, Pagani M, Lombardi F, Cerutti S. Cardiovascular neural regulation explored in the frequency domain. Circulation. 1991;84(2):482–92.
Warner HR, Russell RO Jr. Effect of combined sympathetic and vagal stimulation on heart rate in the dog. Circ Res. 1969;24:567–73.
Sayers BM. Analysis of heart rate variability. Ergonomics. 1973;16(1):17–32.
Haken H. Synergetics: an introduction. Berlin: Springer Verlag; 1983.
Akselrod S, Gordon D, Ubel FA, Shannon DC, Berger AC, Cohen RJ. Power spectrum analysis of heart rate fluctuation: a quantitative probe of beat-to-beat cardiovascular control. Science. 1981;213(4504):220–2.
Schwartz PJ, Pagani M, Lombardi F, Malliani A, Brown AM. A cardiocardiac sympathovagal reflex in the cat. Circ Res. 1973;32(2):215–20.
Pagani M, Lombardi F, Guzzetti S, et al. Power spectral analysis of heart rate and arterial pressure variabilities as a marker of sympatho-vagal interaction in man and conscious dog. Circ Res. 1986;59(2):178–93.
Pagani M, Malliani A. Interpreting oscillations of muscle sympathetic nerve activity and heart rate variability. J Hypertens. 2000;18(s):1709–19.
Mochizuki Y, Shinomoto S. Analog and digital codes in the brain. Phys Rev E. 2014;89(2):022705.
Porta A, Guzzetti S, Montano N, et al. Entropy, entropy rate, and pattern classification as tools to typify complexity in short heart period variability series. IEEE Trans Biomed Eng. 2001;48(11):1282–91.
Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Heart-rate variability: standards of measurements, physiological interpretation and clinical use. Circulation. 1996;93(5):1043–65.
Pagani M, Montano N, Porta A, et al. Relationship between spectral components of cardiovascular variabilities and direct measures of muscle sympathetic nerve activity in humans. Circulation. 1997;95(6):1441–8.
Kandel ER, Schwartz JH, Jessell TM, editors. Principles of neural science. 4th ed. New York: McGraw Hill; 2000.
Massimini M, Porta A, Mariotti M, Malliani A, Montano N. Heart rate variability is encoded in the spontaneous discharge of thalamic somatosensory neurones in cat. J Physiol. 2000;526:387–96.
Voss A, Schroeder R, Heitmann A, Peters A, Perz S. Short-term heart rate variability—influence of gender and age in healthy subjects. PLoS One. 2015;10(3):e0118308.
Benjamin EJ, Blaha MJ, Chiuve SE, et al. Heart disease and stroke statistics-2017 update: a report from the American Heart Association. Circulation. 2017;135(10):e146–603.
Pelliccia F, Kaski JC, Crea F, Camici PG. Pathophysiology of Takotsubo syndrome. Circulation. 2017;135(24):2426–41.
Sala R, Malacarne M, Solaro N, Pagani M, Lucini D. A composite autonomic index as unitary metric for heart rate variability: a proof of concept. Eur J Clin Investig. 2017;47(3):241–9.
Pomeranz B, Macaulay RJ, Caudill MA, et al. Assessment of autonomic function in humans by heart rate spectral analysis. Am J Phys. 1985;248(1 Pt 2):H151–3.
Lucini D, Marchetti I, Spataro A, et al. Heart rate variability to monitor performance in elite athletes: criticalities and avoidable pitfalls. Int J Cardiol. 2017;240:307–12.
Beissner F, Meissner K, Bar KJ, Napadow V. The autonomic brain: an activation likelihood estimation meta-analysis for central processing of autonomic function. J Neurosci. 2013;33(25):10503–11.
Badilini F, Pagani M, Porta A. Heartscope: a software tool addressing autonomic nervous system regulation. Comput Cardiol. 2005;32:259–62.
Saary MJ. Radar plots: a useful way for presenting multivariate health care data. J Clin Epidemiol. 2008;61(4):311–7.
Sala R, Spataro A, Malacarne M, et al. Discriminating between two autonomic profiles related to posture in Olympic athletes. Eur J Appl Physiol. 2016;116(4):815–22.
Engel BT. Psychosomatic medicine, behavioral medicine, just plain medicine. Psychosom Med. 1986;48(7):466–79.
Eckberg DL. Sympathovagal balance: a critical appraisal. Circulation. 1997;96(9):3224–32.
Lucini D, Solaro N, Pagani M. Autonomic differentiation map: a novel statistical tool for interpretation of Heart Rate Variability. Frontiers Physiol. 2018;9:401
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this chapter
Cite this chapter
Pagani, M., Sala, R., Malacarne, M., Lucini, D. (2018). Benchmarking Heart Rate Variability to Overcome Sex-Related Bias. In: Kerkhof, P., Miller, V. (eds) Sex-Specific Analysis of Cardiovascular Function. Advances in Experimental Medicine and Biology, vol 1065. Springer, Cham. https://doi.org/10.1007/978-3-319-77932-4_13
Download citation
DOI: https://doi.org/10.1007/978-3-319-77932-4_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-77931-7
Online ISBN: 978-3-319-77932-4
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)