Comparison of Heart Rate Variability and Pulse Rate Variability of Respiratory Control

  • Yi Han
  • Wen-Chen Lin
  • Sheng-Cheng Huang
  • Cheng-Lun Tsai
  • Kang-Ping LinEmail author
Conference paper
Part of the IFMBE Proceedings book series (IFMBE, volume 64)


Heart rate variability (HRV) can be applied to observe the autonomic nervous system activity of human beings. With the development of wearable device technology, PPG signal is often applied to measure pulse rate, and furthermore to analyze the pulse rate variability (PRV), which is considered to be equivalent to HRV. However, regular heart rate will be affected by breath volume and breath rate that causes different results when HRV and PRV are measured in different situations. This paper presents a study based on the characteristics of the respiratory sinus arrhythmia (RSA), the heart rate and tide volume of breath and the variabilities from PPG and ECG which were measured by PSG simultaneously. Furthermore, according to the measurement of PTT based on PPG and ECG signals, the characteristics of peripheral arteries were evaluated. From the results, it was found that the significance (p < 0.05) is shown between HRV and PRV when normal young groups are controlled at different tide volume of breath. No significance is shown on the PRV obtained from four limbs. In summary, the PRV and HRV show different characteristics at different breath controls. There are no differences between each PRV when PPG signals are obtained from four limbs.


Respiratory sinus arrhythmia Heart rate variability Pulse rate variability Photoplethysmography 



The authors acknowledge the financial support by the Ministry of Science and Technology of Taiwan (MOST 103-2632-E-033-001) and (102-2628-B-182A-001-MY3).


  1. 1.
    J. Allen, “Photoplethysmography and its application in clinical physiological measurement,” Physiol. Meas. 28, R1–R39. 2007.CrossRefGoogle Scholar
  2. 2.
    Task Force of the European Society of Cardiology and The North American Society of Pacing and Electrophysiology, “Heart rate variability—Standards of measurement, physiological interpretation and clinical use,” European Heart Journal, vol. 17, pp. 354–381. 1996.Google Scholar
  3. 3.
    J. L. A. Carvalho, “A Tool for Time-Frequency Analysis of Heart Rate Variability,” (in Portuguese), M.Sc. dissertation, Publication ENE.DM-156N03, Department of Electrical Engineering, Universidade de Brasilia, Brasilia, Brazil, 2003.Google Scholar
  4. 4.
    J. Hayano, A. K. Barros, A. Kamiya, N. Ohte, and F. Yasuma, “Assessment of pulse rate variability by the method of pulse–frequency demodulation,” Biomed. Eng. Online, vol. 4, p. 62, 2005.Google Scholar
  5. 5.
    P. S. McKinley, P. A. Shapiro, E. Bagiella, M. M. Myers, R. E. De Meersman, I. Grant, and R. P. Sloan, “Deriving heart period variability from blood pressure waveforms,” J. Appl. Physiol., vol. 95, no. 4, pp. 1431–1438, Oct. 2003.Google Scholar
  6. 6.
    F. Chang, C. Chang, C. Chiu, S. Hsu, Y. Lin. “Variations of HRV analysis in different approaches,” Comput Cardiol, vol. 34, pp. 17–20. 2007.Google Scholar
  7. 7.
    Asada, H. Harry, et al. “Mobile monitoring with wearable photoplethysmographic biosensors,” Engineering in Medicine and Biology Magazine, vol. 22.3, pp. 28–40. 2003.CrossRefGoogle Scholar
  8. 8.
    A. Schäfer, J. Vagedes, “How accurate is pulse rate variability as an estimate of heart rate variability? A review on studies comparing photoplethysmographic technology with an electrocardiogram,” International Journal of Cardiology, vol. 166, pp. 15–29. 2013.CrossRefGoogle Scholar
  9. 9.
    I. Constant, D. Laude, I. Murat, J.L. Elghozi, “Pulse rate variability is not a surrogate for heart rate variability,” Clin Sci., London, vol. 97, pp. 391–397. 1999.Google Scholar
  10. 10.
    J. K. Triedman, J. P. Saul, “Blood Pressure Modulation by Central Venous Pressure and Respiration Buffering Effects of the Heart Rate Reflexes,” Circulation, vol. 89, pp. 169–179. 1994.CrossRefGoogle Scholar
  11. 11.
    D. Sadhukhan, M. Mitra, “R-peak detection algorithm for ECG using double difference and RR interval processing,” Procedia Technology, vol. 4, pp. 873–877. February 2012.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Yi Han
    • 1
  • Wen-Chen Lin
    • 1
  • Sheng-Cheng Huang
    • 1
  • Cheng-Lun Tsai
    • 2
  • Kang-Ping Lin
    • 1
    Email author
  1. 1.Department of Electrical EngineeringChung Yuan Christian UniversityTaoyuan CityTaiwan
  2. 2.Department of Biomedical EngineeringChung Yuan Christian UniversityTaoyuan CityTaiwan

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