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A New Algorithm to Reduce and Individualize HRV Recording Time

  • Damien SaboulEmail author
  • Christophe Hautier
Image & Signal Processing
Part of the following topical collections:
  1. Image & Signal Processing

Abstract

The aim of the present work was to propose a Smartphone algorithm to analyze, in real time, the evolution of Heart Rate Variability (HRV) in order to individualize and reduce the recording time according to the specificities of each user. During HRV recording, a new RMSSD value is calculated each time a new RR is captured. The recording process stops once an acceptable stability of HRV is reached. This new method was tested on 3 groups of 15 subjects (cardiac patients, sedentary employees and national-level athletes) and compared with the gold standard method (5 min HRV recording time). The RMSSD indices provided by the short method and by the gold standard method (respectively 62.1 ± 43.7 ms vs. 62.7 ± 44.1 ms) showed no significant differences. In addition, a very strong correlation was observed between RMSSD values obtained by the 2 methods (n = 45; R = 0.998; p < 0.001). Routine duration of the new method was significantly shorter with a time-savings of 2 min (178 ± 51 s vs. 300 s; p < 0.05). This new algorithm seems to adapt perfectly to each subject, and it can detect the stability phase for HRV measurements during the recording process. Algorithm provides an adapted and personal routine duration that can evolve each day depending on parameters such as fatigue or stress level that are known to influence HRV. This solution can be easily implemented in a smartphone application and seems particularly suitable for performing daily HRV monitoring in field conditions.

Keywords

HRV Smartphone application Optimization Individualization Recording time Monitoring 

Notes

Compliance with ethical standards

Research involving human participants

The protocol was approved by the ethical committee of France-Sud Est VI (number 2015-A01755–44). In addition, all procedures performed in studies involving human participants were in accordance with the 1964 Helsinki declaration and its later amendments.

Informed consent

Informed consent was obtained from all individual participants included in the study.

References

  1. 1.
    Akselrod, S., Gordon, D., Ubel, F. A., Shannon, D. C., Berger, A. C., and Cohen, R. J., Power spectrum analysis of heart rate fluctuation: A quantitative probe of beat-to-beat cardiovascular control. Science 213(4504):220–222, 1981.CrossRefGoogle Scholar
  2. 2.
    Task-Force, 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(3):354–381, 1996.CrossRefGoogle Scholar
  3. 3.
    Buchheit, M., Monitoring training status with HR measures: Do all roads lead to Rome? Front. Physiol. 5:73, 2014.CrossRefGoogle Scholar
  4. 4.
    Jarczok, M. N., Jarczok, M., Mauss, D., Koenig, J., Li, J., Herr, R. M., and Thayer, J. F., Autonomic nervous system activity and workplace stressors--A systematic review. Neurosci. Biobehav. Rev. 37(8):1810–1823, 2013.CrossRefGoogle Scholar
  5. 5.
    Hautala, A. J., Kiviniemi, A. M., and Tulppo, M. P., Individual responses to aerobic exercise: The role of the autonomic nervous system. Neurosci. Biobehav. Rev. 33(2):107–115, 2009.CrossRefGoogle Scholar
  6. 6.
    Kiviniemi, A. M., Hautala, A. J., Kinnunen, H., Nissila, J., Virtanen, P., Karjalainen, J., and Tulppo, M. P., Daily exercise prescription on the basis of HR variability among men and women. Med. Sci. Sports Exerc. 42(7):1355–1363, 2010.CrossRefGoogle Scholar
  7. 7.
    Behrens, K., Hottenrott, K., Weippert, M., Montanus, H., Kreuzfeld, S., Rieger, A., Lubke, J., Werdan, K., and Stoll, R., Individualization of exercise load control for inpatient cardiac rehabilitation. Development and evaluation of a HRV-based intervention program for patients with ischemic heart failure. Herz 40(Suppl 1):61–69, 2014.PubMedGoogle Scholar
  8. 8.
    Cinaz, B., Arnrich, B., La Marca, R., and Tröster, G., Monitoring of mental workload levels during an everyday life office-work scenario. Pers. Ubiquit. Comput. 17(2):229–239, 2011.  https://doi.org/10.1007/s00779-011-0466-1.CrossRefGoogle Scholar
  9. 9.
    Plews, D., Laursen, P., Stanley, J., Kilding, A., and Buchheit, M., Training adaptation and heart rate variability in elite endurance athletes: Opening the door to effective monitoring. Sports Med. 43(9):773–781, 2013.CrossRefGoogle Scholar
  10. 10.
    Plews, D. J., Laursen, P. B., Le Meur, Y., Hausswirth, C., Kilding, A. E., and Buchheit, M., Monitoring training with heart rate-variability: How much compliance is needed for valid assessment? Int. J. Sports Physiol. Perform. 9(5):783–790, 2013.CrossRefGoogle Scholar
  11. 11.
    Le Meur, Y., Pichon, A., Schaal, K., Schmitt, L., Louis, J., Gueneron, J., Vidal, P. P., and Hausswirth C., Evidence of parasympathetic hyperactivity in functionally overreached athletes. Med. Sci. Sports Exerc. 45(11):2061–71, 2013.Google Scholar
  12. 12.
    Plews, D. J., Laursen, P. B., Kilding, A. E., and Buchheit, M., Evaluating training adaptation with heart rate measures: A methodological comparison. Int. J. Sports Physiol. Perform. 8(6):688–91, 2013.Google Scholar
  13. 13.
    Altini, M., and Amft, O., HRV4Training: Large-scale longitudinal training load analysis in unconstrained free-living settings using a smartphone application. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2016:2610–2613, 2016.PubMedGoogle Scholar
  14. 14.
    Flatt, A. A., and Esco, M. R., Validity of the athlete smart phone application for determining ultra-short-term heart rate variability. J. Hum. Kinet. 39:85–92, 2013.CrossRefGoogle Scholar
  15. 15.
    Perrotta, A. S., Jeklin, A. T., Hives, B. A., Meanwell, L. E., and Warburton, D. E. R., Validity of the elite HRV smartphone application for examining heart rate variability in a field-based setting. J. Strength Cond. Res. 31(8):2296–2302, 2017.CrossRefGoogle Scholar
  16. 16.
    Esco, M. R., and Flatt, A. A., Ultra-short-term heart rate variability indexes at rest and post-exercise in athletes: Evaluating the agreement with accepted recommendations. J. Sports Sci. Med. 13(3):535, 2014.PubMedPubMedCentralGoogle Scholar
  17. 17.
    Flatt, A. A., and Esco, M. R., Heart rate variability stabilization in athletes: Towards more convenient data acquisition. Clin. Physiol. Funct. Imaging 36(5):331–336, 2016.CrossRefGoogle Scholar
  18. 18.
    Bourdillon, N., Schmitt, L., Yazdani, S., Vesin, J. M., and Millet, G. P., Minimal window duration for accurate HRV recording in athletes. Front. Neurosci. 11:456, 2017.CrossRefGoogle Scholar
  19. 19.
    Saboul, D., Pialoux, V., and Hautier, C., The breathing effect of the LF/HF ratio in the heart rate variability measurements of athletes. Eur. J. Sport Sci 14(Suppl 1):S282–S288, 2014.CrossRefGoogle Scholar
  20. 20.
    Saboul, D., Pialoux, V., and Hautier, C., The impact of breathing on HRV measurements: Implications for the longitudinal follow-up of athletes. Eur. J. Sport Sci. 13(5):534–542, 2013.Google Scholar
  21. 21.
    Stanley, J., Peake, J., and Buchheit, M., Cardiac parasympathetic reactivation following exercise: Implications for training prescription. Sports Med. 43(12):1259–1277, 2013.CrossRefGoogle Scholar
  22. 22.
    Nakamura, F. Y., Flatt, A. A., Pereira, L. A., Ramirez-Campillo, R., Loturco, I., and Esco, M. R., Ultra-short-term heart rate variability is sensitive to training effects in team sports players. J. Sports Sci. Med. 14(3):602–605, 2015.PubMedPubMedCentralGoogle Scholar
  23. 23.
    Pereira, L. A., Flatt, A. A., Ramirez-Campillo, R., Loturco, I., and Nakamura, F. Y., Assessing shortened field-based heart-rate-variability-data acquisition in team-sport athletes. Int. J. Sports Physiol. Perform. 11(2):154–158, 2016.CrossRefGoogle Scholar
  24. 24.
    Plews, D. J., Scott, B., Altini, M., Wood, M., and Kilding, A. E., Laursen, P. B., Comparison of heart rate variability recording with smart phone photoplethysmographic, polar H7 chest strap and electrocardiogram methods. Int. J. Sports Physiol. Perform. 12(10):1324–1328, 2017.Google Scholar
  25. 25.
    Bland, J. M., and Altman, D. G., Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1(8476):307–310, 1986.CrossRefGoogle Scholar
  26. 26.
    Murad, K., Brubaker, P. H., Fitzgerald, D. M., Morgan, T. M., Goff, Jr., D. C., Soliman, E. Z., Eggebeen, J. D., and Kitzman, D. W., Exercise training improves heart rate variability in older patients with heart failure: A randomized, controlled, single-blinded trial. Congest. Heart Fail. 18(4):192–197, 2012.CrossRefGoogle Scholar
  27. 27.
    Orsila, R., Virtanen, M., Luukkaala, T., Tarvainen, M., Karjalainen, P., Viik, J., Savinainen, M., and Nygård, C.-H., Perceived mental stress and reactions in heart rate variability--A pilot study among employees of an electronics company. Int. J. Occup. Saf. Ergon. 14(3):275–283, 2008.CrossRefGoogle Scholar
  28. 28.
    Aubert, A. E., Seps, B., and Beckers, F., Heart rate variability in athletes. Sports Med. 33(12):889–919, 2003.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Be-Studys, a Brand of Be-Ys GroupVernier – ChâtelaineSwitzerland
  2. 2.EA 7424 - Inter-university Laboratory of Human Movement Science (LIBM)University of Lyon, University Claude Bernard Lyon 1LyonFrance

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