Advertisement

Comparison of short-term heart rate variability indexes evaluated through electrocardiographic and continuous blood pressure monitoring

  • Riccardo PerniceEmail author
  • Michal Javorka
  • Jana Krohova
  • Barbora Czippelova
  • Zuzana Turianikova
  • Alessandro Busacca
  • Luca Faes
  • Member, IEEE
Original Article
  • 32 Downloads

Abstract

Heart rate variability (HRV) analysis represents an important tool for the characterization of complex cardiovascular control. HRV indexes are usually calculated from electrocardiographic (ECG) recordings after measuring the time duration between consecutive R peaks, and this is considered the gold standard. An alternative method consists of assessing the pulse rate variability (PRV) from signals acquired through photoplethysmography, a technique also employed for the continuous noninvasive monitoring of blood pressure. In this work, we carry out a thorough analysis and comparison of short-term variability indexes computed from HRV time series obtained from the ECG and from PRV time series obtained from continuous blood pressure (CBP) signals, in order to evaluate the reliability of using CBP-based recordings in place of standard ECG tracks. The analysis has been carried out on short time series (300 beats) of HRV and PRV in 76 subjects studied in different conditions: resting in the supine position, postural stress during 45° head-up tilt, and mental stress during computation of arithmetic test. Nine different indexes have been taken into account, computed in the time domain (mean, variance, root mean square of the successive differences), frequency domain (low-to-high frequency power ratio LF/HF, HF spectral power, and central frequency), and information domain (entropy, conditional entropy, self entropy). Thorough validation has been performed using comparison of the HRV and PRV distributions, robust linear regression, and Bland–Altman plots. Results demonstrate the feasibility of extracting HRV indexes from CBP-based data, showing an overall relatively good agreement of time-, frequency-, and information-domain measures. The agreement decreased during postural and mental arithmetic stress, especially with regard to band-power ratio, conditional, and self-entropy. This finding suggests to use caution in adopting PRV as a surrogate of HRV during stress conditions.

Graphical abstract

Keywords

Heart rate variability (HRV) Pulse rate variability (PRV) Electrocardiography (ECG) Photoplethysmography (PPG) Continuous blood pressure (CBP) Time series analysis 

Notes

Acknowledgments

The research has been supported by the grant ASTONISH, H2020-EU.2.1.1.7. (ECSEL), University of Palermo, and grants APVV-0235-12, VEGA 1/0117/17, VEGA 1/0202/16, and project “Biomedical Center Martin” ITMS code no. 26220220187, the project co-financed from EU sources.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Informed consent

Informed consent was obtained from all individual participants included in the study. All participants signed a written informed consent, and when the subject was a minor (age < 18 years) prior parental or legal guardian permission was gathered to allow the child to participate in the study. All the procedures were approved by the Ethical Committee of the Jessenius Faculty of Medicine, Comenius University, Martin, Slovakia.

Supplementary material

11517_2019_1957_MOESM1_ESM.docx (771 kb)
ESM 1 (DOCX 770 kb)

References

  1. 1.
    Shaffer F, Ginsberg JP (2017) An overview of heart rate variability metrics and norms. Front Public Health 5(258):1–17Google Scholar
  2. 2.
    Rajendra Acharya U, Paul Joseph K, Kannathal N, Min Lim C, Suri JS (2007) Heart rate variability: a review. Med Bio Eng Comput 44(12):1031–1051Google Scholar
  3. 3.
    Javorka M, Krohova J, Czippelova B, Turianikova Z, Lazarova Z, Wiszt R, Faes L (2018) Towards understanding the complexity of cardiovascular oscillations: insights from information theory. Comput Biol Med 98:48–57Google Scholar
  4. 4.
    Porta A, Di Rienzo M, Wessel N, Kurths J (2009) Addressing the complexity of cardiovascular regulation. Phil Trans A Math Phys Eng Sci 367:1215–1218Google Scholar
  5. 5.
    Costa M, Priplata AA, Lipsitz LA, Wu Z, Huang NE, Goldberger AL, Peng CK (2007) Noise and poise: enhancement of postural complexity in the elderly with a stochastic-resonance-based therapy. Europhys Lett 77(6):68008Google Scholar
  6. 6.
    Malik M, Bigger J, Camm A, Kleiger R (1996) Heart rate variability. Standards of measurement, physiological interpretation, and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Eur Heart J 17:354–381Google Scholar
  7. 7.
    Porta A, De Maria B, Bari V, Marchi A, Faes L (2017) Are nonlinear model-free approaches for the assessment of the entropy-based complexity of the cardiac control superior to a linear model-based one? IEEE Trans Biomed Eng 64(6):1287–1296Google Scholar
  8. 8.
    Taelman J, Vandeput S, Vlemincx E, Spaepen A, Van Huffel S (2011) Instantaneous changes in heart rate regulation due to mental load in simulated office work. Eur J Appl Physiol 111(7):1497–1505Google Scholar
  9. 9.
    Akselrod S, Gordon D, Ubel F, Shannon D, Berger A, Cohen R (1981) Power spectrum analysis of heart rate fluctuation: a quantitative probe of beat-to-beat cardiovascular control. Science (80-.) 213(4504):220–222Google Scholar
  10. 10.
    Porta A, Guzzetti S, Furlan R, Gnecchi-Ruscone T, Montano N, Malliani A (2007b) Complexity and nonlinearity in short-term heart period variability: comparison of methods based on local nonlinear prediction. IEEE Trans Biomed Eng 54(1):94–106Google Scholar
  11. 11.
    Valente M, Javorka M, Porta A, Bari V, Krohova J, Czippelova B, Turianikova Z, Nollo G, Faes L (2018) Univariate and multivariate conditional entropy measures for the characterization of short-term cardiovascular complexity under physiological stress. Physiol Meas 39(1):014002Google Scholar
  12. 12.
    Richman JS, Moorman JR (2000) Physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol Heart Circ Physiol 278(6):H2039–H2049Google Scholar
  13. 13.
    Porta A, Gnecchi-Ruscone T, Tobaldini E, Guzzetti S, Furlan R, Montano N (2007a) Progressive decrease of heart period variability entropy-based complexity during graded head-up tilt. J Appl Physiol 103(4):1143–1149Google Scholar
  14. 14.
    Porta A, Baselli G, Liberati D, Montano N, Cogliati C, Gnecchi-Ruscone T, Malliani A, Cerutti S (1998) Measuring regularity by means of a corrected conditional entropy in sympathetic outflow. Biol Cybern 78(1):71–78Google Scholar
  15. 15.
    Wibral M, Lizier JT, Priesemann V (2015) Bits from brains for biologically inspired computing. Front robot AI 2:5Google Scholar
  16. 16.
    Sun Y, Thakor N (2016) Photoplethysmography revisited: from contact to noncontact, from point to imaging. IEEE Trans Biomed Eng 63(3):463–477Google Scholar
  17. 17.
    Allen J (2007) Photoplethysmography and its application in clinical physiological measurement. Physiol Meas 28(3):R1–R39Google Scholar
  18. 18.
    Agrò D, Canicattì R, Tomasino A, Giordano A, Adamo G, Parisi A, Pernice R, Stivala S, Giaconia C, Busacca AC, Ferla G (2014) PPG embedded system for blood pressure monitoring. Proc of 2014 AEIT Int Ann Conf, Trieste, Italy, pp 1–6Google Scholar
  19. 19.
    Oreggia D, Guarino S, Parisi A, Pernice R, Adamo G, Mistretta L, Di Buono P, Fallica G, Ferla G, Cino AC, Giaconia C, Busacca AC (2015) Physiological parameters measurements in a cardiac cycle via a combo PPG-ECG system. Proc of 2015 AEIT Int Ann Conf, Napoli, Italy, pp 1–6Google Scholar
  20. 20.
    Siddiqui A, Zhang Y, Feng Z, Kos A (2016) A pulse rate estimation algorithm using PPG and smartphone camera. J Med Syst 40(5):126Google Scholar
  21. 21.
    Bánhalmi A, Borbás J, Fidrich M, Bilicki V, Gingl Z, Rudas L (2018) Analysis of a pulse rate variability measurement using a smartphone camera. J Healthc Eng 2018(4038034):15Google Scholar
  22. 22.
    Wesseling KH (1996) Finger arterial pressure measurement with Finapres. Z Kardiol 85(Suppl 3):38–44Google Scholar
  23. 23.
    Imholz BPM, Wieling W, Van Montfrans GA, Wesseling KH (1998) Fifteen years experience with finger arterial pressure monitoring: assessment of the technology. Cardiovasc Res 38(3):605–616Google Scholar
  24. 24.
    Schäfer A, Vagedes J (2013) How accurate is pulse rate variability as an estimate of heart rate variability? A review on studies comparing photoplethysmographic technology with an electrocardiogram. Int J Cardiol 166(1):15–29Google Scholar
  25. 25.
    Hayes MJ, Smith PR (1998) Artifact reduction in photoplethysmography. Appl Opt 37(31):7437–7446Google Scholar
  26. 26.
    Gil E, Orini M, Bailón R, Vergara JM, Mainardi L, Laguna P (2010) Photoplethysmography pulse rate variability as a surrogate measurement of heart rate variability during non-stationary conditions. Physiol Meas 31(9):1271–1290Google Scholar
  27. 27.
    Lu G, Yang F, Taylor JA, Stein JF (2009) A comparison of photoplethysmography and ECG recording to analyse heart rate variability in healthy subjects. J Med Eng Technol 33(8):634–641Google Scholar
  28. 28.
    Rauh R, Limley R, Bauer RD, Radespiel-Troger M, Mueck-Weymann M (2004) Comparison of heart rate variability and pulse rate variability detected with photoplethysmography. Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Proceedings Volume 5474, Saratov Fall Meeting 2003: Optical Technologies in Biophysics and Medicine V, pp 115–126Google Scholar
  29. 29.
    Giardino ND, Lehrer PM, Edelberg R (2002) Comparison of finger plethysmograph to ECG in the measurement of heart rate variability. Psychophysiology 39(2):246–253Google Scholar
  30. 30.
    McKinley PS, Shapiro PA, Bagiella E, Myers MM, De Meersman RE, Grant I, Sloan RP (2003) Deriving heart period variability from blood pressure waveforms. J Appl Physiol 95(4):1431–1438Google Scholar
  31. 31.
    Carrasco S, Gonzalez R, Jimenez J, Roman R, Medina V, Azpiroz J (1998) Comparison of the heart rate variability parameters obtained from the electrocardiogram and the blood pressure wave. J Med Eng Technol 22(5):195–205Google Scholar
  32. 32.
    Dawson SL, Panerai RB, Potter JF (1998) Should one use electrocardiographic or Finapres-derived pulse intervals for calculation of cardiac baroreceptor sensitivity? Blood Press Monit 3(5):315–320Google Scholar
  33. 33.
    Suhrbier A, Heringer R, Walther T, Malberg H, Wessel N (2006) Comparison of three methods for beat-to-beat-interval extraction from continuous blood pressure and electrocardiogram with respect to heart rate variability analysis. Biomed Tech (Berl) 51(2):70–76Google Scholar
  34. 34.
    Lu S, Zhao H, Ju K, Shin K, Lee M, Shelley K, Chon KH (2008) Can photoplethysmography variability serve as an alternative approach to obtain heart rate variability information? J Clin Monit Comput 22(1):23–29Google Scholar
  35. 35.
    Khandoker AH, Karmakar CK, Palaniswami M (2011) Comparison of pulse rate variability with heart rate variability during obstructive sleep apnea. Med Eng Phys 33(2):204–209Google Scholar
  36. 36.
    Iozzia L, Cerina L, Mainardi L (2016) Relationships between heart-rate variability and pulse-rate variability obtained from video-PPG signal using ZCA. Physiol Meas 37(11):1934–1944Google Scholar
  37. 37.
    Hernando D, Roca S, Sancho j AA, Bailón R (2018) Validation of the AppleWatch for heart rate variability measurements during relax and mental stress in healthy subjects. Sensors (Basel) 18(8):E2619Google Scholar
  38. 38.
    Pernice R, Javorka M, Krohova J, Czippelova B, Turianikova Z, Busacca A, Faes L (2018) Reliability of short-term heart rate variability indexes assessed through photoplethysmography. Proc of 40th Ann Int Conf of the IEEE engineering in medicine and biology society (EMBC 2018), Honolulu, USA, pp 5610–5613Google Scholar
  39. 39.
    Javorka M, Krohova J, Czippelova B, Turianikova Z, Lazarova Z, Javorka K, Faes L (2017) Basic cardiovascular variability signals: mutual directed interactions explored in the information domain. Physiol Meas 38(5):877–894Google Scholar
  40. 40.
    Vollmer M (2015) A robust, simple and reliable measure of heart rate variability using relative RR intervals. Proc 2015 Computing in Cardiology Conf (CinC), Nice, France, pp 609–612Google Scholar
  41. 41.
    Magagnin V, Bassani T, Bari V, Turiel M, Maestri R, Pinna GD, Porta A (2011) Non-stationarities significantly distort short-term spectral, symbolic and entropy heart rate variability indices. Physiol Meas 32(11):1775–1786Google Scholar
  42. 42.
    Dantas EM, Sant'Anna ML, Andreão RV, Gonçalves CP, Morra EA, Baldo MP, Rodrigues SL, Mill JG (2012) Spectral analysis of heart rate variability with the autoregressive method: what model order to choose? Comput Biol Med 42(2):164–170Google Scholar
  43. 43.
    Marple SL Jr (1987) Digital spectral analysis with applications. New Jersey: Prentice Hall, Englewood CliffsGoogle Scholar
  44. 44.
    Baselli G, Porta A, Cerutti S (1997) Spectral decomposition in multichannel recordings based on multivariate parametric identification. IEEE Trans Biomed Eng 44(11):1092–1101Google Scholar
  45. 45.
    McCraty R, Shaffer F (2015) Heart rate variability: new perspectives on physiological mechanisms, assessment of self-regulatory capacity, and health risk. Glob Adv Health Med 4(1):46–61Google Scholar
  46. 46.
    Draghici AE, Taylor JA (2016) The physiological basis and measurement of heart rate variability in humans. J Physiol Anthropol 35(1):22Google Scholar
  47. 47.
    Bari V, Girardengo G, Marchi A, De Maria B, Brink PA, Crotti L, Schwartz PJ, Porta A (2015) A refined multiscale self-entropy approach for the assessment of cardiac control complexity: application to long QT syndrome type 1 patients. Entropy 17(11):7768–7785Google Scholar
  48. 48.
    Xiong W, Faes L, Ivanov PC (2017) Entropy measures, entropy estimators and their performance in quantifying complex dynamics: effects of artifacts, nonstationarity and long-range correlations. Phys Rev E 95:62114Google Scholar
  49. 49.
    Faes L, Kugiumtzis D, Nollo G, Jurysta F, Marinazzo D (2015) Estimating the decomposition of predictive information in multivariate systems. Phys Rev E Stat Nonlinear Soft Matter Phys 91(3):032904Google Scholar
  50. 50.
    Giavarina D (2015) Understanding Bland Altman analysis. Biochem Med (Zagreb) 25(2):141–151Google Scholar
  51. 51.
    Chen X, Huang YY, Yun F, Chen TJ, Li J (2015) Effect of changes in sympathovagal balance on the accuracy of heart rate variability obtained from photoplethysmography. Exp Ther Med 10(6):2311–2318Google Scholar
  52. 52.
    Chan GS, Middleton PM, Celler BG, Wang L, Lovell NH (2007) Change in pulse transit time and pre-ejection period during head-up tilt-induced progressive central hypovolaemia. J Clin Monit Comput 21(5):283–293Google Scholar
  53. 53.
    Schneider GM, Jacobs DW, Gevirtz RN, O’Connor DT (2003) Cardiovascular haemodynamic response to repeated mental stress in normotensive subjects at genetic risk of hypertension: evidence of enhanced reactivity, blunted adaptation, and delayed recovery. J Hum Hypertens 17(12):829–840Google Scholar
  54. 54.
    Krohova J, Czippelova B, Turianikova Z, Lazarova Z, Tonhajzerova I, Javorka M (2017) Preejection period as a sympathetic activity index: a role of confounding factors. Physiol Res 66(Supplementum 2):S265–S275Google Scholar
  55. 55.
    Proença J, Muehlsteff J, Aubert X, Carvalho P (2010) Is pulse transit time a good indicator of blood pressure changes during short physical exercise in a young population? Proc of 32nd Ann Int Conf of the IEEE engineering in medicine and biology society (EMBC 2010), Buenos Aires, Argentina, pp 598–601Google Scholar
  56. 56.
    Naschitz JE, Bezobchuk S, Mussafia-Priselac R, Sundick S, Dreyfuss D, Khorshidi I, Karidis A, Manor H, Nagar M, Peck ER, Peck S, Storch S, Rosner I, Gaitini L (2004) Pulse transit time by R-wave-gated infrared photoplethysmography: review of the literature and personal experience. J Clin Monit Comput 18(5–6):333–342Google Scholar
  57. 57.
    Ma H, Zhang Y (2006) Spectral analysis of pulse transittime variability and itscoherence with other cardiovascular variabilities. Conf Proc IEEE Eng Med Biol Soc 1:6442–6445Google Scholar
  58. 58.
    Foo J, Lim C (2006) Pulse transit time as an indirect marker for variations in cardiovascular related reactivity. Technol Health Care 14(2):97–108Google Scholar
  59. 59.
    Wang R, Jia W, Mao Z-H, Sclabassi RJ, Sun M (2014) cuff-free blood pressure estimation using pulse transit time and heart rate. Int Conf signal process proc, ZangZhou, China, pp 115–118Google Scholar
  60. 60.
    Mukkamala R, Hahn JO, Inan OT, Mestha LK, Kim CS, Toreyin K, Kyal S (2015) Towards ubiquitous blood pressure monitoring via pulse transit time: theory and practice. IEEE Trans Biomed Eng 62(8):1879–1901Google Scholar
  61. 61.
    Martin SLO, Carek AM, Kim CS, Ashouri H, Inan OT, Hahn JO, Mukkamala R (2016) Weighing scale-based pulse transit time is a superior marker of blood pressure than conventional pulse arrival time. Sci Rep 6:39273Google Scholar
  62. 62.
    Montano N, Ruscone TG, Porta A, Lombardi F, Pagani M, Malliani A (1994) Power spectrum analysis of heart rate variability to assess the changes in sympathovagal balance during graded orthostatic tilt. Circulation 90(4):1826–1831Google Scholar
  63. 63.
    Cohen MA, Taylor JA (2002) Short-term cardiovascular oscillations in man: measuring and modelling the physiologies. J Physiol 542(Pt 3):669–683Google Scholar
  64. 64.
    Faes L, Porta A, Rossato G, Adami A, Tonon D, Corica A, Nollo G (2013) Investigating the mechanisms of cardiovascular and cerebrovascular regulation in orthostatic syncope through an information decomposition strategy. Auton Neurosci 178(1–2):76–82Google Scholar
  65. 65.
    Faes L, Porta A, Nollo G, Javorka M (2017) Information decomposition in multivariate systems: definitions, implementation and application to cardiovascular networks. Entropy 19(1):5Google Scholar
  66. 66.
    Saul JP, Berger RD, Albrecht P, Stein SP, Chen MH, Cohen RJ (1991) Transfer function analysis of the circulation: unique insights into cardiovascular regulation. Am J Physiol Heart Circ Physiol 261(4 Pt 2):H1231–H1245Google Scholar

Copyright information

© International Federation for Medical and Biological Engineering 2019

Authors and Affiliations

  1. 1.Department of EngineeringUniversity of PalermoPalermoItaly
  2. 2.Department of Physiology and the Biomedical Center Martin, Jessenius Faculty of MedicineComenius University in BratislavaMartinSlovakia

Personalised recommendations