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access icon openaccess Are ultra-short heart rate variability features good surrogates of short-term ones? State-of-the-art review and recommendations

This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)
Received 03/10/2017, Accepted 14/02/2018, Revised 15/12/2017, Published 14/03/2018

Ultra-short heart rate variability (HRV) analysis refers to the study of HRV features in excerpts of length <5 min. Ultra-short HRV is widely growing in many healthcare applications for monitoring individual's health and well-being status, especially in combination with wearable sensors, mobile phones, and smart-watches. Long-term (nominally 24 h) and short-term (nominally 5 min) HRV features have been widely investigated, physiologically justified and clear guidelines for analysing HRV in 5 min or 24 h are available. Conversely, the reliability of ultra-short HRV features remains unclear and many investigations have adopted ultra-short HRV analysis without questioning its validity. This is partially due to the lack of accepted algorithms guiding investigators to systematically assess ultra-short HRV reliability. This Letter critically reviewed the existing literature, aiming to identify the most suitable algorithms, and harmonise them to suggest a standard protocol that scholars may use as a reference in future studies. The results of the literature review were surprising, because, among the 29 reviewed papers, only one paper used a rigorous method, whereas the others employed methods that were partially or completely unreliable due to the incorrect use of statistical tests. This Letter provides recommendations on how to assess ultra-short HRV features reliably and proposes an inclusive algorithm that summarises the state-of-the-art knowledge in this area.

Inspec keywords: medical signal processing; electrocardiography; patient monitoring; statistical analysis

Other keywords: statistical tests; ultrashort heart rate variability features; healthcare applications; smart watches; short-term heart rate variability features; HRV; health monitoring; mobile phones; wearable sensors

Subjects: Probability theory, stochastic processes, and statistics; Digital signal processing; Electrodiagnostics and other electrical measurement techniques; Biology and medical computing; Other topics in statistics; Electrical activity in neurophysiological processes; Other topics in statistics; Bioelectric signals; Signal processing and detection

References

    1. 1)
      • 1. Fenici, R., Brisinda, D., Sorbo, A.R.: ‘Methods for real-time assessment of operational stress during realistic police tactical training’ in Kitaeff, J. (Ed.): ‘Handbook of police psychology’ (Routledge, New York, 2011), pp. 295–319.
    2. 2)
    3. 3)
    4. 4)
    5. 5)
    6. 6)
    7. 7)
      • 41. Salahuddin, L., Jeong, M.G., Kim, D.: ‘Ultra short term analysis of heart rate variability using normal sinus rhythm and atrial fibrillation ECG data’. 2007 9th Int. Conf. on e-Health Networking, Application and Services, 2007, pp. 240–243.
    8. 8)
    9. 9)
      • 4. Morelli, D., Bartoloni, L., Colombo, M., et al: ‘Profiling the propagation of error from PPG to HRV features in a wearable physiological-monitoring device’, Healthc. Technol. Lett., 2017, pp. 1–6, doi: 10.1049/htl.2017.0039.
    10. 10)
    11. 11)
      • 48. Li, Z., Chines, A., Meredith, M.: ‘Statistical validation of surrogate endpoints: is bone density a valid surrogate for fracture?’, J. Musculoskeletal Neuronal Interact., 2004, 4, p. 64.
    12. 12)
    13. 13)
    14. 14)
    15. 15)
      • 45. Dewitte, K., Fierens, C., Stöckl, D., et al: ‘Application of the Bland–Altman plot for interpretation of method-comparison studies: a critical investigation of its practice’, Clin. Chem., 2002, 48, pp. 799–801.
    16. 16)
    17. 17)
      • 32. Thong, T., Li, K., McNames, J., et al: ‘Accuracy of ultra-short heart rate variability measures’. Proc. 25th Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society, 2003, Cancun, Mexico, September 2003, pp. 2424–2427.
    18. 18)
    19. 19)
      • 23. Sun, F.-T., Kuo, C., Cheng, H.-T., et al: ‘Activity-aware mental stress detection using physiological sensors’. Int. Conf. on Mobile Computing, Applications, and Services, Santa Clara, CA, USA, October 2010, pp. 211–230.
    20. 20)
    21. 21)
    22. 22)
    23. 23)
    24. 24)
      • 26. Choi, J., Gutierrez-Osuna, R.: ‘Using heart rate monitors to detect mental stress’. Sixth Int. Workshop on Wearable and Implantable Body Sensor Networks, 2009. BSN 2009, Berkeley, CA, USA, June 2009, pp. 219–223.
    25. 25)
    26. 26)
    27. 27)
    28. 28)
    29. 29)
    30. 30)
    31. 31)
    32. 32)
      • 16. Mayya, S., Jilla, V., Tiwari, V.N., et al: ‘Continuous monitoring of stress on smartphone using heart rate variability’. 2015 IEEE 15th Int. Conf. on Bioinformatics and Bioengineering (BIBE), Belgrade, Serbia, November 2015, pp. 1–5.
    33. 33)
      • 18. Wijsman, J., Grundlehner, B., Liu, H., et al: ‘Towards mental stress detection using wearable physiological sensors’. 2011 Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society, EMBC, Boston, MA, USA, 30 August–3 September 2011, pp. 1798–1801.
    34. 34)
      • 20. Salahuddin, L., Cho, J., Jeong, M.G., et al: ‘Ultra short term analysis of heart rate variability for monitoring mental stress in mobile settings’. Conf. Proc. IEEE Eng. Medicine Biology Society, Lyon, France, August 2007, pp. 4656–4659.
    35. 35)
      • 19. Kim, D., Seo, Y., Cho, J., et al: ‘Detection of subjects with higher self-reporting stress scores using heart rate variability patterns during the day’. 30th Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society, 2008. EMBS 2008, Vancouver, BC, Canada, August 2008, pp. 682–685.
    36. 36)
      • 44. Vidakovic, B.: ‘Statistics for bioengineering sciences: with MATLAB and WinBUGS support’ (Springer Science & Business Media, New York, NY, USA, 2011).
    37. 37)
      • 14. Boonnithi, S., Phongsuphap, S.: ‘Comparison of heart rate variability measures for mental stress detection’, Comput. Cardiol., 2011, 2011, pp. 85–88.
    38. 38)
    39. 39)
    40. 40)
      • 43. Benjamini, Y., Hochberg, Y.: ‘Controlling the false discovery rate: a practical and powerful approach to multiple testing’, J. R. Stat. Soc. B, Methodol., 1995, 57, (1), pp. 289–300.
    41. 41)
      • 47. Macbeth, G., Razumiejczyk, E., Ledesma, R.D.: ‘Cliff's delta calculator: a non-parametric effect size program for two groups of observations’, Universitas Psychol., 2011, 10, pp. 545–555.
    42. 42)
    43. 43)
      • 30. Esco, M.R., 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., 2014, 13, pp. 535–541.
    44. 44)
    45. 45)
    46. 46)
    47. 47)
      • 8. Massaro, S., Pecchia, L.: ‘Heart rate variability (HRV) analysis: a methodology for organizational neuroscience’, Org. Res. Methods, 2016, doi: 10.1177/1094428116681072.
    48. 48)
      • 40. Arza, A., Garzón, J., Hemando, A., et al: ‘Towards an objective measurement of emotional stress: preliminary analysis based on heart rate variability’. 2015 37th Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society (EMBC), Milan, Italy, August 2015, pp. 3331–3334.
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