Annals of Biomedical Engineering

, Volume 41, Issue 12, pp 2617–2629 | Cite as

A Technical Assessment of Pulse Wave Velocity Algorithms Applied to Non-invasive Arterial Waveforms

  • N. R. Gaddum
  • J. Alastruey
  • P. Beerbaum
  • P. Chowienczyk
  • T. Schaeffter
Article

Abstract

Non-invasive assessment of arterial stiffness through pulse wave velocity (PWV) analysis is becoming common clinical practice. However, the effects of measurement noise, temporal resolution and similarity of the two waveforms used for PWV calculation upon accuracy and variability are unknown. We studied these effects upon PWV estimates given by foot-to-foot, least squared difference, and cross-correlation algorithms. We assessed accuracy using numerically generated blood pressure and flow waveforms for which the theoretical PWV was known to compare with the algorithm estimates. We assessed variability using clinical measurements in 28 human subjects. Wave shape similarity was quantified using a cross correlation-coefficient (CCCoefficient), which decreases with increasing distance between waveform measurements sites. Based on our results, we propose the following criteria to identify the most accurate and least variable algorithm given the noise, resolution and CCCoefficient of the measured waveforms. (1) Use foot-to-foot when the noise-to-signal ratio ≤10%, and/or temporal resolution ≥100 Hz. Otherwise (2) use a least squares differencing method applied to the systolic upstroke.

Keywords

Pulse wave velocity Foot to foot Least squares Cross correlation Doppler ultrasound Tonometry One-dimensional modelling 

Notes

Acknowledgments

This work was supported by Medical Research Council, grant G09000865 (NG), British Heart Foundation (BHF) Intermediate Basic Science Research Fellowship (FS/09/030/27812) and the Centre of Excellence in Medical Engineering funded by the Wellcome Trust and EPSRC under Grant No. WT 088641/Z/09/Z (JA). This research was also supported by the National Institute for Health Research (NIHR) Biomedical Research Centre at Guy’s and St Thomas’ NHS Foundation Trust and King’s College London. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health.

References

  1. 1.
    Alastruey, J., A. W. Khir, K. S. Matthys, et al. Pulse wave propagation in a model human arterial network: assessment of 1-D visco-elastic simulations against in vitro measurements. J. Biomech. 44(12):2250–2258, 2011.PubMedCrossRefGoogle Scholar
  2. 2.
    Benthin, M., P. Dahl, R. Ruzicka, and K. Lindström. Calculation of pulse-wave velocity using cross correlation—effects of reflexes in the arterial tree. Ultrasound Med. Biol. 17(5):461–496, 1991.PubMedCrossRefGoogle Scholar
  3. 3.
    Bramwell, J. C. and A. V. Hill. The velocity of the pulse wave in man. In: Royal Society of London. Series B, Containing Papers of a Biological Character. The Royal Society, 1922.Google Scholar
  4. 4.
    Cheng, C. P., R. J. Herfkens, and C. A. Taylor. Abdominal aortic hemodynamic conditions in healthy subjects aged 50–70 at rest and during lower limb exercise: in vivo quantification using MRI. Atherosclerosis 168(2):323–331, 2003.PubMedCrossRefGoogle Scholar
  5. 5.
    Chiu, Y. C., P. W. Arand, S. G. Shroff, T. Feldman, and J. D. Carroll. Determination of pulse wave velocities with computerized algorithms. Am. Heart J. 121(5):1460–1470, 1991.PubMedCrossRefGoogle Scholar
  6. 6.
    Dogui, A., A. Redheuil, M. Lefort, et al. Measurement of aortic arch pulse wave velocity in cardiovascular MR: comparison of transit time estimators and description of a new approach. J. Magn. Reson. Imaging 33(6):1321–1329, 2011.PubMedCrossRefGoogle Scholar
  7. 7.
    Fielden, S. W., B. K. Fornwalt, M. Jerosch-Herold, R. L. Eisner, A. E. Stillman, and J. N. Oshinski. A new method for the determination of aortic pulse wave velocity using cross-correlation on 2D PCMR velocity data. J. Magn. Reson. Imaging 27(6):1382–1387, 2008.PubMedCrossRefGoogle Scholar
  8. 8.
  9. 9.
    Ibrahim, E.-S. H., K. R. Johnson, A. B. Miller, J. M. Shaffer, and R. D. White. Measuring aortic pulse wave velocity using high-field cardiovascular magnetic resonance: Comparison of techniques. J. Cardiovasc. Magn. Reson. 12:26, 2010.CrossRefGoogle Scholar
  10. 10.
    Latham, R. D., N. Westerhof, P. Sipkema, B. J. Rubal, P. Reuderink, and J. P. Murgo. Regional wave travel and reflections along the human aorta. Circulation 72(6):1257–1269, 1985.PubMedCrossRefGoogle Scholar
  11. 11.
    Laurent, S., and P. Boutouyrie. Recent advances in arterial stiffness and wave reflection in human hypertension. Hypertension 49:1202–1206, 2007.PubMedCrossRefGoogle Scholar
  12. 12.
    Laurent, S., J. Cockcroft, L. Van Bortel, et al. Expert consensus document on arterial stiffness: methodological issues and clinical applications. Eur. Heart J. 27:2588–2605, 2006.PubMedCrossRefGoogle Scholar
  13. 13.
    Lehmann, E. D., K. D. Hopkins, A. Rawesh, et al. Relation between number of cardiovascular risk factors/events and noninvasive Doppler ultrasound assessments of aortic compliance. Hypertension 32(3):565–569, 1998.PubMedCrossRefGoogle Scholar
  14. 14.
    Malindzak, Jr, G. S. Reflection of pressure pulses in the aorta. Med. Res. Eng. 6(4):25–31, 1967.PubMedGoogle Scholar
  15. 15.
    Matthys, K. S., J. Alastruey, J. Peiró, et al. Pulse wave propagation in a model human arterial network: assessment of 1-D numerical simulations against in vitro measurements. J. Biomech. 40(15):3476–3486, 2007.PubMedCrossRefGoogle Scholar
  16. 16.
    Mitchell, G. F., M. A. Pfeffer, P. V. Finn, and J. M. Pfeffer. Comparison of techniques for measuring pulse-wave velocity in the rat. J. Appl. Physiol. 82(1):203–210, 1997.PubMedGoogle Scholar
  17. 17.
    Moens, A. I. Die pulskurve. 1878: Leiden.Google Scholar
  18. 18.
    Nichols, W. W., M. F. O’Rourke, and C. Vlachopoulos. Mcdonald’s blood flow in arteries. Theoretical, experimental and clinical principles (6th ed.). London: Arnold, 2011.Google Scholar
  19. 19.
    Reymond, P., F. Merenda, F. Perren, D. Rufenacht, and N. Stergiopulos. Validation of a one-dimensional model of the systemic arterial tree. Am. J. Physiol. Heart Circ. Physiol. 297(1):H208–H222, 2009.PubMedCrossRefGoogle Scholar
  20. 20.
    Todd, B. S., and D. C. Andrews. The identification of peaks in physiological signals. Comput. Biomed. Res. 32:322–335, 1999.PubMedCrossRefGoogle Scholar
  21. 21.
    Vardoulis, O., T. G. Papaioannou, and N. Stergiopulos. On the estimation of total arterial compliance from aortic pulse wave velocity. Ann. Biomed. Eng. 40(12):2619–2626, 2012.PubMedCrossRefGoogle Scholar
  22. 22.
    Vlachopoulos, C., K. Aznaouridis, M. F. O’Rourke, M. E. Safar, K. Baou, and C. Stefanadis. Prediction of cardiovascular events and all-cause mortality with central haemodynamics: a systematic review and meta-analysis. Eur. Heart J. 31(15):1865–1871, 2010.PubMedCrossRefGoogle Scholar
  23. 23.
    Zuo, J. L., Y. Li, Z. J. Yan, et al. Validation of the central blood pressure estimation by the sphygmocor system in Chinese. Blood Press. Monit. 15(5):268–274, 2010.PubMedCrossRefGoogle Scholar

Copyright information

© Biomedical Engineering Society 2013

Authors and Affiliations

  • N. R. Gaddum
    • 1
  • J. Alastruey
    • 1
  • P. Beerbaum
    • 2
  • P. Chowienczyk
    • 3
  • T. Schaeffter
    • 1
  1. 1.Division of Imaging Sciences and Biomedical Engineering, St. Thomas’ HospitalKing’s College LondonLondonUK
  2. 2.Department of Pediatric Cardiology & Pediatric Intensive Care MedicineHannover Medical SchoolHannoverGermany
  3. 3.St Thomas’ HospitalKing’s College London British Heart Foundation CentreLondonUK

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