Advertisement

Comparison of the Correlation of Different Pulse Transit Time Parameters to Blood Pressure

  • Wan-Hua Lin
  • Oluwarotimi Williams Samuel
  • Qing Liu
  • Yuan-Ting Zhang
  • Guanglin Li
Conference paper
Part of the IFMBE Proceedings book series (IFMBE, volume 64)

Abstract

Estimation of blood pressure (BP) based on pulse transit time (PTT) is of great interest since it can estimate BP continuously and cufflessly. In previous studies, different character points were available in ECG and in photoplethysmogram (PPG) for calculating PTT. The present study aimed at comparing the correlation of BP to different PTT parameters calculated using different character points of ECG and PPG. PTT parameters were calculated as the time interval from R peak, Q valley, or S valley of ECG to the peak or valley of the first derivative of the PPG. Correlations of beat-to-beat BP to the different beat-to-beat PTT parameters were calculated for the selected 13 datasets with a total of 3910 heart beats data. The results showed that the PTT as the time interval from Q valley of the ECG to the peak of the first derivative of the PPG gave the best parameter which correlates with both the systolic blood pressure (SBP, r = −0.62 ± 0.14) and the diastolic blood pressure (DBP, r = −0.45 ± 0.18). Therefore, this method of determining PTT would be useful to improve the accuracy of estimating BP continuously and cufflessly.

Notes

Acknowledgements

The work was supported in part by the National Key Basic Research Program of China (NO. 2013CB329505), the National Natural Science Foundation of China under Grants (NO. 61135004, NO. 61203209), the Shenzhen Governmental Basic Research Grant (NO. JCYJ20130402113127532), and the External Cooperation Program of Chinese Academy of Sciences (NO. GJHZ1212).

Conflict of Interest

The authors declare that they have no conflict of interest.

References

  1. 1.
    C. Ahlstrom, et al., “Noninvasive investigation of blood pressure changes using the pulse wave transit time: a novel approach in the monitoring of hemodialysis patients,” J Artif Organs, vol. 8, pp. 192–7, 2005.CrossRefGoogle Scholar
  2. 2.
    F. Barcelo-Rico, et al., “Adaptive calibration algorithm for plasma glucose estimation in continuous glucose monitoring,” IEEE J Biomed Health Inform, vol. 17, pp. 530–8, May 2013.CrossRefGoogle Scholar
  3. 3.
    Y. Choi, et al., “Noninvasive cuffless blood pressure estimation using pulse transit time and Hilbert-Huang transform,” Computers & Electrical Engineering, vol. 39, pp. 103–111, Jan 2013.CrossRefGoogle Scholar
  4. 4.
    H. Gesche, et al., “Continuous blood pressure measurement by using the pulse transit time: comparison to a cuff-based method,” European Journal of Applied Physiology, vol. 112, pp. 309–315, Jan 2012.CrossRefGoogle Scholar
  5. 5.
    I. C. Jeong and J. Finkelstein, “Optimizing Non-Invasive Blood Pressure Estimation Using Pulse Transit Time,” Medinfo 2013: Proceedings of the 14th World Congress on Medical and Health Informatics, Pts 1 and 2, vol. 192, pp. 1198–1198, 2013.Google Scholar
  6. 6.
    Y. Li, et al., “Characters available in photoplethysmogram for blood pressure estimation: beyond the pulse transit time,” Australas Phys Eng Sci Med, vol. 37, pp. 367–76, Jun 2014.CrossRefGoogle Scholar
  7. 7.
    Q. Liu, et al., “Attenuation of systolic blood pressure and pulse transit time hysteresis during exercise and recovery in cardiovascular patients,” IEEE Trans Biomed Eng, vol. 61, pp. 346–52, Feb 2014.Google Scholar
  8. 8.
    C. C. Poon and Y. T. Zhang, “Cuff-less and noninvasive measurements of arterial blood pressure by pulse transit time,” Conf Proc IEEE Eng Med Biol Soc, vol. 6, pp. 5877–80, 2005.Google Scholar
  9. 9.
    S. Puke, et al., “Blood pressure estimation from pulse wave velocity measured on the chest,” Conf Proc IEEE Eng Med Biol Soc, vol. 2013, pp. 6107–10, 2013.Google Scholar
  10. 10.
    X. F. Teng and Y. T. Zhang, “An evaluation of a PTT-based method for noninvasive and cuffless estimation of arterial blood pressure,” Conf Proc IEEE Eng Med Biol Soc, vol. 1, pp. 6049–52, 2006.Google Scholar
  11. 11.
    M. Y. Wong, et al., “An evaluation of the cuffless blood pressure estimation based on pulse transit time technique: a half year study on normotensive subjects,” Cardiovasc Eng, vol. 9, pp. 32–8, Mar 2009.CrossRefGoogle Scholar
  12. 12.
    Q. Liu, et al., “Time-frequency analysis of variabilities of heart rate, systolic blood pressure and pulse transit time before and after exercise using the recursive autoregressive model,” Biomedical Signal Processing and Control, vol. 6, pp. 364–369, Oct 2011.CrossRefGoogle Scholar
  13. 13.
    J. Proenca, et al., “Is Pulse Transit Time a good indicator of Blood Pressure changes during short physical exercise in a young population?,” 2010 Annual International Conference of the Ieee Engineering in Medicine and Biology Society (Embc), pp. 598–601, 2010.Google Scholar
  14. 14.
    H. Fukushima, et al., “Cuffless Blood Pressure Estimation using only Photoplethysmography based on Cardiovascular parameters,” 2013 35th Annual International Conference of the Ieee Engineering in Medicine and Biology Society (EMBC), pp. 2132–2135, 2013.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Wan-Hua Lin
    • 1
    • 2
  • Oluwarotimi Williams Samuel
    • 1
    • 2
  • Qing Liu
    • 3
  • Yuan-Ting Zhang
    • 3
    • 4
  • Guanglin Li
    • 1
  1. 1.Key Laboratory of Human-Machine Intelligence-Synergy SystemsShenzhen Institutes of Advanced Technology, Chinese Academy of SciencesShenzhenChina
  2. 2.Shenzhen College of Advanced TechnologyUniversity of Chinese Academy of SciencesShenzhenChina
  3. 3.Joint Research Center for Biomedical EngineeringThe Chinese University of Hong KongShatinHong Kong
  4. 4.Apple Inc.Silicon ValleyUSA

Personalised recommendations