Methodological framework for heart rate variability analysis during exercise: application to running and cycling stress testing

  • David HernandoEmail author
  • Alberto Hernando
  • Jose A. Casajús
  • Pablo Laguna
  • Nuria Garatachea
  • Raquel Bailón
Original Article


Standard methodologies of heart rate variability analysis and physiological interpretation as a marker of autonomic nervous system condition have been largely published at rest, but not so much during exercise. A methodological framework for heart rate variability (HRV) analysis during exercise is proposed, which deals with the non-stationary nature of HRV during exercise, includes respiratory information, and identifies and corrects spectral components related to cardiolocomotor coupling (CC). This is applied to 23 male subjects who underwent different tests: maximal and submaximal, running and cycling; where the ECG, respiratory frequency and oxygen consumption were simultaneously recorded. High-frequency (HF) power results largely modified from estimations with the standard fixed band to those obtained with the proposed methodology. For medium and high levels of exercise and recovery, HF power results in a 20 to 40% increase. When cycling, HF power increases around 40% with respect to running, while CC power is around 20% stronger in running.


Cardiolocomotor coupling Stride cadence Pedalling cadence Non-stationary analysis 



This work is supported by the CIBER in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN) through Instituto de Salud Carlos III, by MINECO and FEDER under project TIN2014-53567-R, by Grupo Consolidado BSICoS ref:T96 from DGA and European Social Fund (EU). The computation was performed by the ICTS NANBIOSIS, more specifically by the High Performance Computing Unit of the CIBER-BBN at the University of Zaragoza.


  1. 1.
    Bailón R, Garatachea N, de la Iglesia I, Casajús JA, Laguna P (2013) Influence of running stride frequency in heart rate variability analysis during treadmill exercise testing. IEEE Trans Biomed Eng 60 (7):1796–1805CrossRefPubMedGoogle Scholar
  2. 2.
    Bailón R, Laguna P, Mainardi L, Sörnmo L (2007) Analysis of heart rate variability using time-varying frequency bands based on respiratory frequency. In: 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC07), vol 29, pp 6674–6677Google Scholar
  3. 3.
    Bailón R, Laouini G, Grao C, Orini M, Laguna P, Meste O (2011) The integral pulse frequency modulation with time–varying threshold: application to heart rate variability analysis during exercise stress testing. IEEE Trans Biomed Eng 58(3):642–652CrossRefPubMedGoogle Scholar
  4. 4.
    Bailón R, Mainardi L, Orini M, Sörnmo L, Laguna P (2010) Analysis of heart rate variability during exercise stress testing using respiratory information. Biomed Signal Process Control 5:299–310CrossRefGoogle Scholar
  5. 5.
    Bailón R, Serrano P, Laguna P (2011) Influence of time-varying mean heart rate in coronary artery disease diagnostic performance of heart rate variability indices from exercise stress testing. J Electrocardiol 44:445–452CrossRefPubMedGoogle Scholar
  6. 6.
    Blain G, Meste O, Blain A, Bermon S (2009) Time–frequency analysis of heart rate variability reveals cardiolocomotor coupling during dynamic cycling exercise in humans. Amer J Physiol Heart Circ Physiol 296:1651–1659CrossRefGoogle Scholar
  7. 7.
    Borresen J, Lambert M (2008) Autonomic control of heart rate during and after exercise: measurements and implications for monitoring training status. Sports Med 38(8):633–646CrossRefPubMedGoogle Scholar
  8. 8.
    Chan HL, Huang HH, Lin JL (2001) Time-frequency analysis of heart rate variability during transient segments. Ann Biomed Eng 29(11):983–996CrossRefPubMedGoogle Scholar
  9. 9.
    Cottin F, Médigue C, Leprêtre P, Papelier Y, Koralsztein J, Billat V (2004) Heart rate variability during exercise performed below and above ventilatory threshold. Med Sci Sports Exerc 36(4):594–600CrossRefPubMedGoogle Scholar
  10. 10.
    Cottin F, Papelier Y (2002) Regulation of cardiovascular system during dynamic exercise: integrative approach. Crit Rev Phys Rehabil Med 14(1):53–81Google Scholar
  11. 11.
    Cottin F, Papelier Y (2008) Effect of heavy exercise on spectral baroreflex sensitivity, heart rate, and blood pressure variability in well-trained humans. Amer J Physiol Heart Circ Physiol 295:H1150–H1155CrossRefGoogle Scholar
  12. 12.
    Drezner JA, Fischbach P, Froelicher V, Marek J, Pelliccia A, Prutkin JM, Schmied CM, Sharma S, Wilson MG, Ackerman MJ, Anderson J, Ashley E, Asplund CA, Baggish AL, Brjesson M, Cannon BC, Corrado D, DiFiori JP, Harmon KG, Heidbuchel H, Owens DS, Paul S, Salerno JC, Stein R, Vetter VL (2013) Normal electrocardiographic findings: recognising physiological adaptations in athletes. Br J Sports Med 47(3):125–136CrossRefPubMedGoogle Scholar
  13. 13.
    Hernando A, Hernando D, Garatachea N, Casajús JA, Bailón R (2015) Attenuation of the influence of cardiolocomotor coupling in heart rate variability interpretation during exercise test. In: 37nd Annual International Conference of the IEEE EMBS: 1508–1511Google Scholar
  14. 14.
    Hernando D, Bailón R, Almeida R, Hernández A (2014) QRS detection optimization in stress test recordings using evolutionary algorithms. XLI International Conference on Computing in Cardiology: 737–740Google Scholar
  15. 15.
    Hottenrott K, Hoos O, Esperer H (2006) Heart rate variability and physical exercise. Current Status Herz 31(6):544–552CrossRefPubMedGoogle Scholar
  16. 16.
    Laguna P, Moody GB, Mark R (1998) Power spectral density of unevenly sampled data by least-square analysis. IEEE Trans Biomed Eng 45(6):698–715CrossRefPubMedGoogle Scholar
  17. 17.
    Llamedo M, Martínez JP (2014) QRS detectors performance comparison in public databases. XLI International Conference on Computing in Cardiology: 357–360Google Scholar
  18. 18.
    Mainardi L (2009) On the quantification of heart rate variability spectral parameters using time–frequency and time-varying methods. Philosophical Transactions of the Royal Society of London A: Mathematical. Phys Eng Sci 367(1887):255–275CrossRefGoogle Scholar
  19. 19.
    Martin W, Flandrin P (1985) Wigner–Ville spectral analysis of nonstationary processes. IEEE Trans Acoust Speech Signal Process 33:1461–1470CrossRefGoogle Scholar
  20. 20.
    Martínez JP, Almeida R, Olmos S, Rocha AP, Laguna P (2004) A wavelet-based ECG delineator: evaluation on standard databases. IEEE Trans Biomed Eng 51(4):570–581CrossRefPubMedGoogle Scholar
  21. 21.
    Mateo J, Laguna P (2003) Analysis of heart rate variability in the presence of ectopic beats using the heart timing signal. IEEE Trans Biomed Eng 50(3):334–343CrossRefPubMedGoogle Scholar
  22. 22.
    Meste O, Khaddoumi B, Blain G, Bermon S (2005) Time-varying analysis methods and models for the respiratory and cardiac system coupling in graded exercise. IEEE Trans Biomed Eng 52(11):1921–1930CrossRefPubMedGoogle Scholar
  23. 23.
    Meste O, Rix H, Blain G (2009) ECG processing for exercise test. Advanced Biosignal Processing. Springer, BerlinGoogle Scholar
  24. 24.
    Millet GP, Vleck VE, Bentley DJ (2009) Physiological differences between cycling and running: lessons from triathletes. Sports Med 39(3):179–206CrossRefPubMedGoogle Scholar
  25. 25.
    Monfredi O, Lyashkov AE, Johnsen AB, Inada S, Schneider H, Wang R, Nirmalan M, Wisloff U, Maltsev VA, Lakatta EG, Zhang H, Boyett MR (2014) Biophysical characterization of the underappreciated and important relationship between heart rate variability and heart rate. Hypertension 64(6):1334–1343CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Orini M, Bailón R, Enk R, Koelsch S, Mainardi L, Laguna P (2010) A method for continuously assessing the autonomic response to music-induced emotions through HRV analysis. Med Biol Eng Comput 48:423–433CrossRefPubMedGoogle Scholar
  27. 27.
    Orini M, Bailón R, Mainardi L, Laguna P (2012) Synthesis of HRV signals characterized by predetermined time-frequency structure by means of time-varying ARMA models. Biomed Signal Process Control 7:141–150CrossRefGoogle Scholar
  28. 28.
    Pradhapan P, Tarvainen M, Nieminen T, Lehtinen R, Nikus K, Lehtimäki T, Kähönen M, Viik J (2014) Effect of heart rate correction on pre- and post-exercise heart rate variability to predict risk of mortality—an experimental study on the FINCAVAS cohort. Front Physiol 5:208. CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Rajendra AU, Paul JK, Kannathal N, Lim C, Suri J (2006) Heart rate variability: a review. Med Biol Eng Comput 44:1031–1051CrossRefGoogle Scholar
  30. 30.
    Sacha J, Barabach S, Statkiewicz-Barabach G, Sacha K, Müller A, Piskorski J (2013) How to strengthen or weaken the HRV dependence on heart rate—description of the method and its perspectives. Int J Cardiol 168:1660–1663CrossRefPubMedGoogle Scholar
  31. 31.
    Sacha J, Pluta W (2005) Which heart rate is more variable: a slow or a fast one? It depends on the method of heart rate variability analysis. Folia Cardiol 12(suppl. D):1–4Google Scholar
  32. 32.
    Sacha J, Pluta W (2008) Alterations of an average heart rate change heart rate variability due to mathematical reasons. Int J Cardiol 128:444–447CrossRefPubMedGoogle Scholar
  33. 33.
    Sarmiento S, García-Manso JM, Martín-González JM, Vaamonde D, Calderón J, Da Silva-Grigoletto ME (2013) Heart rate variability during high-intensity exercise. J Syst Sci Complex 26:104–116CrossRefGoogle Scholar
  34. 34.
    Wasserman K (2011) Principles of exercise testing and interpretation: including pathophysiology and clinical applications. Lippincott Williams & Wilkins, PhiladelphiaGoogle Scholar
  35. 35.
    Working group of ESC (1996) Heart rate variability. standards of measurement, physiological interpretation, and clinical use. Eur Heart J 17:354–381CrossRefGoogle Scholar

Copyright information

© International Federation for Medical and Biological Engineering 2017

Authors and Affiliations

  1. 1.Biomedical Signal Interpretation, Computational Simulation (BSICoS) Group at the Aragón Institute of Engineering Research (I3A), IIS AragónUniversity of ZaragozaZaragozaSpain
  2. 2.Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN)Centro de Investigación Biomédica en Red (CIBER)ZaragozaSpain
  3. 3.Centro Universitario de la Defensa (CUD)Academia General Militar (AGM)ZaragozaSpain
  4. 4.Departamento de Fisiatría y Enfermería, Facultad de Ciencias de la Salud y del Deporte, GENUD (Growth, Exercise, Nutrition and Development) research groupInstituto Agroalimentario de Aragon IA2 (Universidad de Zaragoza-CITA), IIS Aragón, ZaragozaZaragozaSpain
  5. 5.Centro de Investigación Biomédica en Red de la Fisiopatología de la Obesidad y Nutrición (CIBERObn)ZaragozaSpain
  6. 6.EXERNETRed de Investigación en Ejercicio Fisico y Salud para Poblaciones EspecialesZaragozaSpain

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