Skip to main content
SpringerLink
Log in

Heart Rate Variability: Influence of Pre-processing Methods in Identifying Single-Night Sleep-Deprived Subjects

  • Original Article
  • Published:
Journal of Medical and Biological Engineering Aims and scope Submit manuscript

Abstract

Purpose

Autonomic nervous system (ANS) activity can be non-invasively estimated from the heart rate variability (HRV) signal obtained from the electrocardiogram (ECG). The aim of this work is to find useful parameters that allow to establish the presence of a single night sleep deprivation in healthy subjects.

Methods

The study included two experimental groups of subjects: Non-sleep-deprived (sleep ≥ 4 h, N = 13) and sleep-deprived (sleep <4 h, N = 10). The RR series extracted from 5 min resting ECG signals were pre-processed using four different algorithms to detect and edit artifacts. The RR series were analyzed in terms of time-domain, frequency-domain and using the Poincaré plot, in order to determine differences in HRV indexes across domains and subject groups. Statistical analyses were performed with the Friedman and Mann–Whitney tests, along, with correlation analysis.

Results

Pre-processing methods showed a moderate level of agreement. In subjects with 4 h of single-night sleep deprivation, results differed according to the selected method.

Conclusion

SD21 index derived from the Poincaré plot was the only HRV index showing differences between sleep-deprived and non-deprived subjects.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Wild, C., Nichols, E., Battista, M., Stojanoski, B., & Owen, A. (2018). Dissociable effects of self-reported daily sleep duration on high-level cognitive abilities. Sleep, 41(12), 1–11.

    Article  Google Scholar 

  2. Taylor, A. H., & Dorn, L. (2006). Stress, fatigue, health, and risk of road traffic accidents among professional drivers: The contribution of physical inactivity. Annual Review Public Health, 27(1), 371–391.

    Article  Google Scholar 

  3. Alhola, P., & Polo-Kantola, P. (2007). Sleep deprivation: Impact on cognitive performance. Neuropsychiatric Disease Treatment, 3(5), 553–67.

    Google Scholar 

  4. Lo, J. C., Groeger, J. A., Santhi, N., Arbon, E. L., Lazar, A. S., Hasan, S., et al. (2012). Effects of partial and acute total sleep deprivation on performance across cognitive domains, individuals and circadian phase. PLoS ONE, 7, 45987. https://doi.org/10.1371/journal.pone.0045987.

    Article  Google Scholar 

  5. Lowe, C. J., Safati, A., & Hall, P. A. (2007). The neurocognitive consequences of sleep restriction: A meta-analytic review. Neuroscience & Biobehavioral Reviews, 80, 586–604. https://doi.org/10.1016/j.neubiorev.2017.07.010.

    Article  Google Scholar 

  6. Banerjee, I., Ho Lee, J., Jang, K., Pande, S., & Ragland, D.R. (2009). Rest Areas-Reducing Accidents Involving Driver Fatigue. UC Berkeley Traffic Safety Center. Retrieved August 31, 220, from https://dot.ca.gov/-/media/dot-media/programs/research-innovation-system-information/documents/final-reports/ca09-1092-finalreport-a11y.pdf.

  7. Chiu, C. C., Chuang, C. M., & Hsu, C. Y. (2009). Discrete wavelet transform applied on personal identity verification with ECG signal. International Journal of Wavelets, Multiresolution and Information Processing, 7(3), 341–355.

    Article  Google Scholar 

  8. Vigo, D. E., Ogrinz, B., Wan, L., Bersenev, E., Tuerlinckx, F., Van Den Bergh, O., et al. (2012). Sleep-wake differences in heart rate variability during a 105-day simulated mission to Mars. Aviation Space and Environmental Medicine, 83(2), 125–30.

    Article  Google Scholar 

  9. Buendia, R., Forcolin, F., Karlsson, J., Arne Sjöqvist, B., Anund, A., & Candefjord, S. (2019). Deriving heart rate variability indices from cardiac monitoring-An indicator of driver sleepiness. Traffic Injury Prevention, 20(3), 249–254.

    Article  Google Scholar 

  10. Vicente, J., Laguna, P., Bartra, A., & Bailón, R. (2016). Drowsiness detection using heart rate variability. Medical and Biological Engineering and Computing, 54(6), 927–937.

    Article  Google Scholar 

  11. Forcolin, F., Buendia, R., Candefjord, S., Karlsson, J., Sjöqvist, B. A., & Anund, A. (2018). Comparison of outlier heartbeat identification and spectral transformation strategies for deriving heart rate variability indices for drivers at different stages of sleepiness. Traffic Injury Prevention, 19(S1), S111–S119.

    Google Scholar 

  12. Peltola, M. A. (2012). Role of editing of R-R intervals in the analysis of heart rate variability. Frontiers in Physiology, 3, 148. https://doi.org/10.3389/fphys.2012.00148.

    Article  Google Scholar 

  13. Malik, M., & Camm, J. (1995). Heart rate variability. New York: Futura Publishing Company Inc.

    Google Scholar 

  14. Camm, J. (1996). Guidelines heart rate variability. European Heart Journal, 17, 354–381.

    Article  Google Scholar 

  15. Choi, A., & Shin, H. (2018). Quantitative analysis of the effect of an ectopic beat on the heart rate variability in the resting condition. Frontiers in Physiology, 9, 922. https://doi.org/10.3389/fphys.2018.00922.

    Article  Google Scholar 

  16. Sathyapriya, L., Murali, L., & Manigandan, T. (2015). Analysis and detection R-peak detection using Modified Pan-Tompkins algorithm. In IEEE International Conference on Advanced Communications, Control and Computing Technologies.. https://doi.org/10.1109/ICACCCT.2014.7019490.

  17. Aubert, A. E., Ramaekers, D., & Beckers, F. (1999). Analysis of heart rate variability in unrestrained rats, assessment of method and results. Journal of Medical and Biological Engineering, 60, 197–213.

    Google Scholar 

  18. Kemper, K. J., Hamilton, C., & Atkinson, M. (2007). Heart rate variability: Impact of differences in outlier identification and management strategies on common measures in three clinical populations. Pediatric Research, 62(3), 337–342.

    Article  Google Scholar 

  19. McNames, J., Thong, T., & Aboy, M. (2004). Impulse rejection filter for artifact removal in spectral analysis of biomedical signals. In The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. https://doi.org/10.1109/IEMBS.2004.1403112.

  20. Thuraisingham, R. A. (2006). Preprocessing RR interval time series for heart rate variability analysis and estimates of standard deviation of RR intervals. Computer Methods and Programs in Biomedicine, 83(1), 78–82.

    Article  Google Scholar 

  21. Mishra, A., & Swati, D. (2015). The recursive combination filter approach of pre-processing for the estimation of standard deviation of RR series. Australasian Physical & Engineering Sciences in Medicine, 38(3), 413–423.

    Article  Google Scholar 

  22. Rincon Soler, A. I., Silva, L. E. V., Fazan, R., & Murta Junior, L. O. (2017). The impact of artifact correction methods of RR series on heart rate variability parameters. Journal of Applied Physiology, 124(3), 646–652.

    Article  Google Scholar 

  23. Wejer, D., Makowiec, D., Struzik, Z., et al. (2014). Impact of the editing of patterns with abnormal RR intervals on the assessment of heart rate variability. Acta Physica Polonica B, 45(11), 2103.

    Article  Google Scholar 

  24. Laguna, P., Moody, G. B., & Mark, R. G. (1998). Power spectral density of unevenly sampled data by least-square analysis: Performance and application to heart rate signals. IEEE Transactions on Biomedical Engineering, 45(6), 698–715.

    Article  Google Scholar 

  25. Saini, B. S., Singh, D., Uddin, M., & Kumar, V. (2008). Improved power spectrum estimation for RR-interval time series. International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering, 2(10), 154–158.

    Google Scholar 

  26. Stein, P. K., Domitrovich, P. P., Huikuri, H. V., & Kleiger, R. E. (2005). Traditional and nonlinear heart rate variability are each independently associated with mortality after myocardial infarction. Journal of Cardiovascular Electrophysiology, 16(1), 13–20.

    Article  Google Scholar 

  27. Guzik, P., Piskorski, J., Krauze, T., Schneider, R., Wesseling, K. H., Wykretowicz, A., et al. (2007). Correlations between the Poincaré Plot and Conventional Heart Rate Variability Parameters Assessed during Paced Breathing. Journal of Physiological Sciences, 57(1), 63–71.

    Article  Google Scholar 

  28. Hsu, C. H., Tsai, M. Y., Huang, G. S., Lin, T. C., Chen, K. P., Ho, S. T., et al. (2012). Poincaré plot indexes of heart rate variability detect dynamic autonomic modulation during general anesthesia induction. Acta Anaesthesiologica Taiwanica, 50(1), 12–18.

    Article  Google Scholar 

  29. Hoshi, R. A., Pastre, C. M., Vanderlei, L. C. M., & Godoy, M. F. (2013). Poincaré plot indexes of heart rate variability: Relationships with other nonlinear variables. Autonomic Neuroscience: Basic and Clinical, 177(2), 271–274.

    Article  Google Scholar 

  30. Mann, H. B., & Whitney, D. R. (1947). On a test of whether one of two random variables is stochastically larger than the other. Annals of Mathematical Statistics, 18(1), 50–60.

    Article  MathSciNet  Google Scholar 

  31. Salo, M. A., Huikuri, H. V., & Seppanen, T. (2001). Ectopic beats in heart rate variability analysis: Effects of editing on time and frequency domain measures. Annals Noninvasive Electrocardiology, 6(1), 5–17.

    Article  Google Scholar 

  32. Dimitriev, D. A., Saperova, E. V., & Dimitriev, A. D. (2016). State anxiety and nonlinear dynamics of heart rate variability in students. PLoS ONE, 11, e0146131. https://doi.org/10.1371/journal.pone.0146131.

    Article  Google Scholar 

Download references

Acknowledgements

This study was supported by a grant from Agencia Nacional de Promoción Científica y Tecnológica (PICT Start-Up 2013-0710) and the 4th Research Project Accreditation Program (PROAPI) from Catholic University of Argentina (UCA).

Author information

Authors and Affiliations

  1. National Technological University (UTN, FRBA) and National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina

    Jose Gallardo

  2. Department of Science and Technology, Laboratory of Chronobiology, National University of Quilmes and Chronophysiology Lab., Institute for Biomedical Research (BIOMED), Catholic University of Argentina (UCA) and National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina

    Giannina Bellone & Santiago Plano

  3. Chronophysiology Lab., Institute for Biomedical Research (BIOMED), Catholic University of Argentina (UCA) and National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina

    Daniel Vigo

  4. National Technological University (UTN, FRBA), Translational Medicine and Biomedical Engineering Institute (IMTIB) and National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina

    Marcelo Risk

Authors
  1. Jose Gallardo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Giannina Bellone
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Santiago Plano
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Daniel Vigo
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Marcelo Risk
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jose Gallardo.

Ethics declarations

Conflict of interest

The authors declare no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Ethical Approval

This study went through ethics approval from the Research Committee at National University of Quilmes, Buenos Aires, Argentina (04/07/2016/No. 3).

Consent for Publication

The Author warrants and represents that the Contribution does not infringe upon any copyright or other rights, and that it does not contain infringing or other unlawful matter, that he/she was the sole and exclusive owner of the rights herein conveyed to the Publisher.

Informed Consent

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gallardo, J., Bellone, G., Plano, S. et al. Heart Rate Variability: Influence of Pre-processing Methods in Identifying Single-Night Sleep-Deprived Subjects. J. Med. Biol. Eng. 41, 224–230 (2021). https://doi.org/10.1007/s40846-020-00595-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40846-020-00595-8

Keywords

Search

Navigation