Log in

Model-Based Assessment of Cardiovascular Health from Noninvasive Measurements

  • Published:
Annals of Biomedical Engineering Aims and scope Submit manuscript

Abstract

Cardiovascular health is currently assessed through a variety of hemodynamic parameters, many of which can only be determined by invasive measurement often requiring hospitalization. A noninvasive method of evaluating several of these parameters such as systemic vascular resistance (SVR), maximum left ventricular elasticity E_LV end diastolic volume V ED and cardiac output, is presented. The method has three elements: (1) a distributed model of the human cardiovascular system (Ozawa [et_al.], Ann. Biomed. Eng. 29:284–297, 2001) to generate a solution library that spans the anticipated range of parameter values, (2) a method for establishing the multidimensional relationship between features computed from the arterial blood pressure and/or flow traces (e.g., mean arterial pressure, pulse amplitude, mean flow velocity) and the critical hemodynamic parameters, and (3) a parameter estimation method that yields the best fit between measured and computed data. Sensitivity analyses were used to determine the critical parameters, and the influence of fixed model parameters. Using computer-generated brachial pressure and velocity profiles (which can be measured noninvasively), the error associated with this method was found to be less than 3% for SVR, and less than 10% for E LV and V ED Simulations were also performed to test the ability of the approach to predict changes in SVR and E LV from an initial base line state. © 2002 Biomedical Engineering Society.

PAC2002: 8719Hh, 8719Uv

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

Access this article

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Similar content being viewed by others

REFERENCES

  1. Adams, H. R., C. R. Baxter, and S. D. Izenberg. Decreased contractility and compliance of the left ventricle as complications of thermal trauma. Am. Heart J. 108:1477–1487, 1984.

    Google Scholar 

  2. Belani, K., M. Ozaki, J. Hynson, T. Hartmann, H. Reyford, J. M. Martino, M. Poliac, and R. Miller. A new noninvasive method to measure blood pressure: Results of a multicenter trial. Anesthesiology 91:686–692, 1999.

    Google Scholar 

  3. Barton, R., and J. Ivey. Nelder-mead simplex modifications for simulation optimization. Management Sci. 42:954–973, 1996.

    Google Scholar 

  4. Beards, S. C., and J. Lipman. Decreased cardiac index as an indicator of tension pneumothorax in the ventilated patient. Anaesthesia 49:137–141, 1994.

    Google Scholar 

  5. Cameron, J. D., B. P. McGrath, and A. M. Dart. Use of radial artery applanation tonometry and a generalized transfer function to determine aortic pressure augmentation in subjects with treated hypertension. J. Am. Coll. Cardiol. 32:1214–1220, 1998.

    Google Scholar 

  6. Franke, R., and G. M. Nielson. Scattered data interpolation and applications: A tutorial and study. In: Geometric Modeling: Methods and Applications, edited by H. Hagen and D. Roller. Berlin: Springer, 1990, pp. 131–166.

    Google Scholar 

  7. Guyton, A. C., and J. E. Hall. Textbook of Medical Physiology. Philadelphia: W. B. Saunders Company, 1996, p. 118.

    Google Scholar 

  8. Kasalicky, J., J. Fabian, J. Ressl, P. Jebavy, and V. Stanek. Left ventricular end-diastolic volume in advanced ischemic heart disease: Comparison between healthy subjects and patients with mitral stenosis. Cardiology 60:86–97, 1975.

    Google Scholar 

  9. Kelly, R., C. Hayward, J. Ganis, J. Daley, A. Avolio, and M. O'Rourke. Noninvasive registration of the arterial pressure pulse waveform using high-fidelity applanation tonometry. J. Vasc. Med. Biol. 1:142–149, 1989.

    Google Scholar 

  10. Koelling, T. M., M. J. Semigran, J. Mijller-Ehmsen, U. Schmidt, M. A. Mathier, G. W. Dec, and T. G. Di Salvo. Left ventricular end-diastolic volume index, age, and maximum heart rate at peak exercise predict survival in patients referred for heart transplantation. J. Heart Lung Transplant. 17:278–287, 1998.

    Google Scholar 

  11. Korteweg, D. J. Uber die Fortpflanzungsgeschwindigkeit des Schalles in elastichen Rohren. Ann. Phys. Chem. 5:525–537, 1879.

  12. Lapointe, A. C., F. A. Roberge, R. A. Nadeau, P. S. Thiry, and G. M. Tremblay. Computation of aortic pulse wave velocity and aortic extensibility from pressure gradient measurement. Can. J. Physiol. Pharmacol. 53:940–946, 1975.

    Google Scholar 

  13. McDonald, D. A. Regional pulse wave velocity in the arterial tree. J. Appl. Physiol. 24:73–78, 1968.

    Google Scholar 

  14. Moens, A. I. Die Pulsekurve. Leiden: Brill, 1879.

    Google Scholar 

  15. Morita, S., R. L. Kormos, J. C. Astbury, R. D. Shaub, A. Kawai, and B. P. Griffith. Standardized ejection fraction as a parameter of overall ventricular pump function. Jpn. Circ. J. 64:510–515, 2000.

    Google Scholar 

  16. Omboni, S., A. A. Smit, and W. Wieling. Twenty four hour continuous noninvasive finger blood pressure monitoring: A novel approach to the evaluation of treatment in patients with autonomic failure. Br. Heart J. 73:290–292, 1995.

    Google Scholar 

  17. Ozawa, E. T., K. Bottom, X. **ao, and R. D. Kamm. Numerical simulation of enhanced external counterpulsation. Ann. Biomed. Eng. 29:284–297, 2001.

    Google Scholar 

  18. Ramsey, M. W., W. R. Stewart, and C. J. Jones. Real-time measurement of pulse wave velocity from arterial pressure waveforms. Med. Biol. Eng. Comput. 33:636–642, 1995.

    Google Scholar 

  19. Renka, R. L. Algorithm 660: QSHEP2D: Quadratic Shepard method for bivariate interpolation of scattered data. ACM TOMS 14:149–150, 1988.

    Google Scholar 

  20. Schmidt, B. M., A. Montealegre, C. P. Janson, N. Martin, C. Stein-Kemmesies, A. Scherhag, M. Feuring, M. Christ, and M. Wehling. Short term cardiovascular effects of aldosterone in healthy male volunteers. J. Clin. Endocrinol. Metab. 84:3528–3533, 1999.

    Google Scholar 

  21. Senzaki, H., C. Chen, and D. A. Kass. Single-beat estimation of end-systolic pressure-volume relation in humans. Circulation 94:2497–2506, 1996.

    Google Scholar 

  22. Stergiopulos, N., B. Westerhof, and N. Westerhof. Physical basis of pressure transfer from periphery to aorta: A modelbased study. Am. J. Physiol. 274:H1386, 1998.

    Google Scholar 

  23. Stergiopulos, N., D. Young, and T. Rogge. Computer simulation of arterial flow with applications to arterial and aortic stenoses. J. Biomech. 25:1477–1488, 1992.

    Google Scholar 

  24. Stevenson, L., and J. Tillisch. Maintenance of cardiac output with normal filling pressures in patients with dilated heart failure. Circulation 74:303–308, 1986.

    Google Scholar 

  25. **ao, X. Nonivasive assessment of cardiovascular health. MIT S.M. thesis, 2000.

  26. Yesilyurt, S., and A. T. Patera. Surrogates for numerical simulations: optimization of eddy-promoter heat exchangers. Comput. Methods Appl. Mech. Eng. 121:231–257, 1995.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

**ao, X., Ozawa, E.T., Huang, Y. et al. Model-Based Assessment of Cardiovascular Health from Noninvasive Measurements. Annals of Biomedical Engineering 30, 612–623 (2002). https://doi.org/10.1114/1.1484217

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1114/1.1484217

Navigation