Indirect Blood Pressure Evaluation by Means of Genetic Programming

  • Giovanna SanninoEmail author
  • Ivanoe De Falco
  • Giuseppe De Pietro
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 574)


This paper relies on the hypothesis of the existence of a nonlinear relationship between Electrocardiography (ECG) and Heart Related Variability (HRV) parameters, plethysmography (PPG), and blood pressure (BP) values. This hypothesis implies that, rather than continuously measuring the patient’s BP, both their systolic and diastolic BP values can be indirectly measured as follows: a wearable wireless PPG sensor is applied to a patient’s finger, an ECG sensor to their chest, HRV parameter values are computed, and regression is performed on the achieved values of these parameters. Genetic Programming (GP) is a Computational Intelligence paradigm that can at the same time automatically evolve the structure of a mathematical model and select from among a wide parameter set the most important parameters contained in the model. Consequently, it can carry out very well the task of regression. The scientific literature of this field reveals that nobody has ever used GP aiming at relating parameters derived from HRV analysis and PPG to BP values. Therefore, in this paper we have carried out preliminary experiments on the use of GP in facing this regression task. GP has been able to find a mathematical model expressing a nonlinear relationship between heart activity, and thus ECG and HRV parameters, PPG and BP values. The experimental results reveal that the approximation error involved by the use of this method is lower than 2 mmHg for both systolic and diastolic BP values.


Blood pressure Wearable sensors Heart rate variability Plethysmography Regression Genetic programming 


  1. 1.
    Maguire, S., Rinehart, J., Vakharia, S., Cannesson, M.: Technical communication: respiratory variation in pulse pressure and plethysmographic waveforms: intraoperative applicability in a North American academic center. Anesth. Analg. 112(1), 94–96 (2011)CrossRefGoogle Scholar
  2. 2.
    von Skerst, B.: Market survey, N=198 physicians in Germany and Austria. December 2007 - March 2008. InnoTech Consult GmbH, Germany (2008)Google Scholar
  3. 3.
    Ilies, C., Kiskalt, H., Siedenhans, D., Meybohm, P., Steinfath, M., Bein, B., Hanss, R.: Detection of hypotension during Caesarean section with continuous non-invasive arterial pressure device or intermittent oscillometric arterial pressure measurement. Br. J. Anaesth. 109(3), 413–419 (2012)CrossRefGoogle Scholar
  4. 4.
    Dueck, R., Jameson, L.C.: Reliability of hypotension detection with noninvasive radial artery beat-to-beat versus upper arm cuff BP monitoring. Anesth. Analg. 102(Suppl), S10 (2006)Google Scholar
  5. 5.
  6. 6.
    Koza, J.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)zbMATHGoogle Scholar
  7. 7.
    Meigas, K., Lass, J., Karai, D., Kattai, R., Kaik, J.: Pulse Wave Velocity in Continuous Blood Pressure Measurements. In: IFMBE Proceedings, World Congress on Medical Physics and Biomedical Engineering 2006, Volume 14, pp 626–629. Springer (2007)Google Scholar
  8. 8.
    Najjar, S., Scuteri, A., Shetty, V., Wright, J.G., Muller, D.C., Fleg, J.L., Spurgeon, H.P., Ferrucci, L., Lakatta, E.G.: 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
  9. 9.
    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)Google Scholar
  10. 10.
    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
  11. 11.
    Vukovich, R., Knill, J.: Blood Pressure Homeostasis. In: Case, D., Sonnenblick, E., Laragh, J. (eds.) Captopril and Hypertension, pp. 3–13. Springer, Heidelberg (1980)CrossRefGoogle Scholar
  12. 12.
    Berntson, G.G., Bigger Jr, J.T., Eckberg, D.L., Grossman, P., Kaufmann, P.G., Malik, M., Nagaraja, H.N., Porges, S.W., Saul, J.P., Stone, P.H., van der Molen, M.W.: Heart rate variability: origins, methods, and interpretive caveats. Psychophysiology 34(6), 623–648 (1997)CrossRefGoogle Scholar
  13. 13.
    Task Force of the European Society of Cardiology, the North American Society of Pacing and Electrophysiology: Heart Rate Variability: Standards of Measurement, Physiological Interpretation, and Clinical Use. Circulation 93(5), 1043–1065 (1996)Google Scholar
  14. 14.
    Karapetian, G.K.: Heart Rate Variability as a Non-invasive Biomarker of Sympatho-vagal Interaction and Determinant of Physiologic Thresholds. Doctoral Thesis, Wayne State University (2008)Google Scholar
  15. 15.
    Golparvar, M., Naddafnia, H., Saghaei, M.: Evaluating the relationship between arterial blood pressure changes and indices of pulse oximetric plethysmography. Anesth. Analg. 95(6), 1686–1690 (2002)CrossRefGoogle Scholar
  16. 16.
    Allen, J.: Photoplethysmography and its application in clinical physiological measurement. Physiol. Meas. 28(3), R1 (2007)CrossRefGoogle Scholar
  17. 17.
    Goldberger, A.L., Amaral, L.A.N., Glass, L., Hausdorff, J.M., Ivanov, P.C., Mark, R.G., Mietus, J.E., Moody, G.B., Peng, C.K., Stanley, H.E.: PhysioBank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals. Circulation 101(23), e215–e220 (2000)CrossRefGoogle Scholar
  18. 18.
    Niskanen, J.P., Tarvainen, M.P., Ranta-Aho, P.O., Karjalainen, P.A.: Software for advanced HRV analysis. Comput. Methods Programs Biomed. 76(1), 73–81 (2004)CrossRefGoogle Scholar
  19. 19.
    Tarvainen, M.P., Ranta-Aho, P.O., Karjalainen, P.A.: An advanced detrending method with application to HRV analysis. IEEE Trans. Biomed. Eng. 49(2), 172–175 (2002)CrossRefGoogle Scholar
  20. 20.
    Melillo, P., Bracale, M., Pecchia, L.: Nonlinear heart rate variability features for real-life stress detection. Case study: students under stress due to university examination. Biomed. Eng. Online 10, 96 (2011)CrossRefGoogle Scholar
  21. 21.
    Rajendra Acharya, U., Paul Joseph, K., Kannathal, N., Lim, C.M., Suri, J.S.: Heart rate variability: a review. Med. Biol. Eng. Comput. 44(12), 1031–1051 (2006)CrossRefGoogle Scholar
  22. 22.
    Searson, D.: GPTIPS: Genetic Programming and Symbolic Regression for MATLAB (2009).
  23. 23.
    Sannino, G., Melillo, P., De Pietro, G., Stranges, S., Pecchia, L.: To what extent it is possible to predict falls due to standing hypotension by using HRV and wearable devices?. In: Study Design and Preliminary Results from a Proof-of-Concept Study, pp. 167–170. Springer International Publishing, Ambient Assisted Living and Daily Activities (2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Giovanna Sannino
    • 1
    Email author
  • Ivanoe De Falco
    • 1
  • Giuseppe De Pietro
    • 1
  1. 1.Institue of High Performance Computing and NetworkingCNRNapoliItaly

Personalised recommendations