Advertisement

On Evaluating Blood Pressure Through Photoplethysmography

  • Giovanna Sannino
  • Ivanoe De Falco
  • Giuseppe De Pietro
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 169)

Abstract

This paper investigates the hypothesis that a nonlinear relationship exists between photoplethysmography (PPG) and blood pressure (BP) values. Trueness of this hypothesis would imply that, instead of measuring a patient’s BP in an invasive way, this could be indirectly measured by applying a wearable PPG sensor and by using the results of a regression analysis linking PPG and BP. Genetic Programming (GP) is well suited to find the relationship between PPG and BP, because it automatically evolves the structure of the most suitable explicit mathematical model for a regression task. In this paper, for the first time, some preliminary experiments on the use of GP to explicitly relate PPG and BP values have been performed. For both systolic and diastolic BP values, explicit nonlinear mathematical models have been achieved, involving an approximation error of less than 3 mmHg in both cases.

Keywords

Blood pressure Wearable sensors Photoplethysmography Regression Genetic programming 

References

  1. 1.
    Koza, J.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)MATHGoogle Scholar
  2. 2.
    Meigas, K., Lass, J., Karai, D., Kattai, R., Kaik, J.: Pulse wave velocity in continuous blood pressure measurements. In: Magjarevic, R., Nagel, J.H. (eds.) IFMBE Proceedings, vol. 14, pp. 626–629. Springer, New York (2007)Google Scholar
  3. 3.
    Najjar, S., Scuteri, A., Shetty, V., Wright, J., Muller, D., Fleg, J., Spurgeon, H., Ferrucci, L., Lakatta, E.: Pulse wave velocity is an independent predictor of the longitudinal increase in systolic blood pressure and of incident hypertension in the baltimore longitudinal study of aging. J. Am. Coll. Cardiol. 51(14), 1377–1383 (2008)CrossRefGoogle Scholar
  4. 4.
    Sannino, G., Melillo, P., Stranges, S., De Pietro, G., Pecchia, L.: Blood pressure drop prediction by using HRV measurements in orthostatic hypotension. J. Medical Systems. 39(11), 1–7 (2015)CrossRefGoogle Scholar
  5. 5.
    Inajima, T., Imai, Y., Shuzo, M., Lopez, G., Yanagimoto, S., Iijima, K., Morita, H., Nagai, R., Yahagi, N., Yamada, I.: Relation between blood pressure estimated by pulse wave velocity and directly measured arterial pressure. J. Robot. Mechatron. 24(5), 811–821 (2012)CrossRefGoogle Scholar
  6. 6.
    Gesche, H., Grosskurth, D., Kuechler, G., Patzak, A.: Continuous blood pressure measurement by using the pulse transit time: comparison to a cuff-based method. Eur. J. Appl. Physiol. 112, 309–315 (2012)CrossRefGoogle Scholar
  7. 7.
    Sannino, G., De Falco, I., De Pietro, G.: Non-invasive estimation of blood pressure through genetic programming: preliminary results. In: 8th International Conference on Biomedical Electronics and Devices, pp. 241–249. Scitepress (2015)Google Scholar
  8. 8.
    Searson, D.: GPTIPS: Genetic Programming and Symbolic Regression for MATLAB (2009). http://gptips.sourceforge.net

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2016

Authors and Affiliations

  • Giovanna Sannino
    • 1
  • Ivanoe De Falco
    • 1
  • Giuseppe De Pietro
    • 1
  1. 1.ICAR-CNRNaplesItaly

Personalised recommendations