Non-Invasive Estimation of Blood Pressure through Genetic Programming - Preliminary Results

Giovanna Sannino, Ivanoe De Falco, Giuseppe De Pietro

Abstract

The hypothesis underlying this paper is that a nonlinear relationship exists between Electrocardiography (ECG) and Heart Related Variability (HRV) parameters, plethysmography (PPG), and blood pressure (BP) values. If this hypothesis is true, rather than continuously measuring the patient’s BP, a wearable wireless PPG sensor can be applied to patient’s finger, an ECG sensor to his/her chest, HRV parameter values can be computed and, through regression, both systolic and diastolic BP values can be indirectly measured. Genetic Programming (GP) automatically both evolves the structure of the mathematical model and finds the most important parameters in it. Therefore, it is perfectly suited to perform regression task. As far as it can be found in the scientific literature of this field, until now nobody has ever investigated the use of GP to relate 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 approximation error involved by the use of this method is lower than 2 mmHg for both systolic and diastolic BP values.

References

  1. Allen, J. (2007). Photoplethysmography and its application in clinical physiological measurement. Physiological measurement, 28(3), R1.
  2. Berntson, G. G., Bigger, J. T., Jr., Eckberg, D. L., Grossman, P., Kaufmann, P. G., Malik, M., . . . van der Molen, M. W. (1997). Heart rate variability: origins, methods, and interpretive caveats. Psychophysiology, 34(6), 623-648.
  3. Dueck, R., Jameson, L. C. (2006). Reliability of hypotension detection with noninvasive radial artery beat-to-beat versus upper arm cuff BP monitoring. Anesth Analg, 102, Suppl:S10.
  4. Electrophysiology, Task, Force, of, the, European, . . . Pacing. (1996). Heart Rate Variability: Standards of Measurement, Physiological Interpretation, and Clinical Use. Circulation, 93(5), 1043-1065. doi: 10.1161/01.cir.93.5.1043.
  5. Gesche, H., Grosskurth, D., Kuechler, G., Patzak, A. (2012). Continuous blood pressure measurement by using the pulse transit time: comparison to a cuffbased method. Eur J Appl Physiol, 112, 309-315.
  6. Goldberger, A. L., Amaral, L. A. N., Glass, L., Hausdorff, J. M., Ivanov, P. C., Mark, R. G., . . . Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation, 101(23), e215-e220. doi: 10.1161/01.CIR.101.23.e215.
  7. Golparvar, M., Naddafnia, H., Saghaei, & Mahmood. (2002). Evaluating the Relationship Between Arterial Blood Pressure Changes and Indices of Pulse Oximetric Plethysmography. Anesthesia & Analgesia, 95(6), 1686-1690 1610.1097/00000539-200212000- 200200040.
  8. Ilies, C., Kiskalt, H., Siedenhans, D., Meybohm, P., Steinfath, M., Bein, B., Hanss, R. (2012). Detection of hypotension during Caesarean section with continuous non-invasive arterial pressure device or intermittent oscillometric arterial pressure measurement. British Journal of Anaesthesia, 3-9.
  9. Inajima, T., Imai, Y., Shuzo, M., Lopez, G., Yanagimoto, S., Iijima, K., Morita, H., Nagai, R., Yahagi, N., Yamada, I. (2012). Relation Between Blood Pressure Estimated by Pulse Wave Velocity and Directly Measured Arterial Pressure. Journal of Robotics andMechatronics, 24(5), 811-821.
  10. Karapetian, G. K., Evaluation, W. S. U. E., & Research. (2008). Heart Rate Variability as a Non-invasive Biomarker of Sympatho-vagal Interaction and Determinant of Physiologic Thresholds: Wayne State University.
  11. Koza, J. (1992). Genetic Programming: On the Programming of Computers by Means of Natural Selection: MIT Press, Cambridge, MA.
  12. Maguire, S., Rinehart, J., Vakharia, S., Cannesson, M. (2011). Technical communication: respiratory variation in pulse pressure and plethysmographic waveforms: intraoperative applicability in a North American academic center. Anesthesia and analgesia, 112(1), 94-96.
  13. Meigas, K., Lass, J., Karai, D., Kattai, R., Kaik, J. (2007). Pulse Wave Velocity in Continuous Blood Pressure Measurements. Paper presented at the IFMBE.
  14. Melillo, P., Bracale, M., & Pecchia, L. (2011). Nonlinear Heart Rate Variability features for real-life stress detection. Case study: students under stress due to university examination. Biomed Eng Online, 10, 96. doi: 10.1186/1475-925x-10-96.
  15. Najjar, S., Scuteri, A., Shetty, V., Wright, J.G., Muller, D.C:, Fleg, J.L., Spurgeon, H.P., Ferrucci, L., Lakatta, E.G. (2008). 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.
  16. Niskanen, J. P., Tarvainen, M. P., Ranta-Aho, P. O., & Karjalainen, P. A. (2004). Software for advanced HRV analysis. Comput Methods Programs Biomed, 76(1), 73-81. doi: 10.1016/j.cmpb.2004.03.004.
  17. Rajendra Acharya, U., Paul Joseph, K., Kannathal, N., Lim, C. M., & Suri, J. S. (2006). Heart rate variability: a review. Med Biol Eng Comput, 44(12), 1031-1051. doi: 10.1007/s11517-006-0119-0.
  18. Tarvainen, M. P., Ranta-Aho, P. O., & Karjalainen, P. A. (2002). An advanced detrending method with application to HRV analysis. IEEE Trans Biomed Eng, 49(2), 172-175. doi: 10.1109/10.979357.
  19. von Skerst, B. (2008). Market survey, N=198 physicians in Germany and Austria. Dec.2007 - Mar 2008: InnoTech Consult GmbH, Germany.
  20. Vukovich, R., & Knill, J. (1980). Blood Pressure Homeostasis. In D. Case, E. Sonnenblick & J. Laragh (Eds.), Captopril and Hypertension (pp. 3-13): Springer US.
Download


Paper Citation


in Harvard Style

Sannino G., De Falco I. and De Pietro G. (2015). Non-Invasive Estimation of Blood Pressure through Genetic Programming - Preliminary Results . In Proceedings of the International Conference on Biomedical Electronics and Devices - Volume 1: SmartMedDev, (BIOSTEC 2015) ISBN 978-989-758-071-0, pages 241-249. DOI: 10.5220/0005318002410249


in Bibtex Style

@conference{smartmeddev15,
author={Giovanna Sannino and Ivanoe De Falco and Giuseppe De Pietro},
title={Non-Invasive Estimation of Blood Pressure through Genetic Programming - Preliminary Results},
booktitle={Proceedings of the International Conference on Biomedical Electronics and Devices - Volume 1: SmartMedDev, (BIOSTEC 2015)},
year={2015},
pages={241-249},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005318002410249},
isbn={978-989-758-071-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Biomedical Electronics and Devices - Volume 1: SmartMedDev, (BIOSTEC 2015)
TI - Non-Invasive Estimation of Blood Pressure through Genetic Programming - Preliminary Results
SN - 978-989-758-071-0
AU - Sannino G.
AU - De Falco I.
AU - De Pietro G.
PY - 2015
SP - 241
EP - 249
DO - 10.5220/0005318002410249