Biomedical Engineering Letters

, Volume 9, Issue 3, pp 395–406 | Cite as

A new approach for blood pressure estimation based on phonocardiogram

  • Tahar OmariEmail author
  • Fethi Bereksi-Reguig
Original Article


Continuous and non-invasive measurement of blood pressure (BP) is of great importance particularly for patients in critical state. To achieve continuous and cuffless BP monitoring, pulse transit time (PTT) has been reported as a potential parameter. Nevertheless, this approach remains very sensitive, cumbersome and disagreeable in ambulatory measurement. This paper proposes a new approach to estimate blood pressure through PCG signal by exploring the correlation between PTT and diastolic duration (S21). In this purpose, an artificial neural network was developed using as input data: (systolic duration, diastolic duration, heart rate, sex, height and weight). According to the NN decision, the mean blood pressure was measured and consequently the systolic and the diastolic pressures were estimated. The proposed method is evaluated on 37 subjects. The obtained results are satisfactory, where, the error in the estimation of the systolic and the diastolic pressures compared to the commercial blood pressure device was in the order of \(6.48 \pm 4.48\) mmHg and \(3.91 \pm 2.58\) mmHg, respectively, which are very close to the AAMI standard, \(5 \pm 8\) mmHg. This shows the feasibility of estimating of blood pressure using PCG.


Blood pressure Systolic duration Diastolic duration Phonocardiogram signal PCG Pulse transit time PTT 



This research was partially supported by Boumerdes university-Algeria. We thank our colleagues from both Tlemcen university and boumerdes university who provided insight and expertise that greatly assisted the research.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in these studies involving human participants were in accordance with the ethical standards of the institutional research committee and with the Helsinki declaration and its later amendments or comparable ethical standards. The following ethical issues were honored

Informed consent

The participants were informed about the purpose of the study and their participation. The participants were also informed that their participation was voluntary and that they were free to withdraw their participation at any time.

Confidentiality and Anonymity

In order to ensure confidentiality and anonymity, the identity ofthe participants was kept anonymous. To this effect, a fictitiousnames were used.


was sought from the department principal to allow the use ofnecessery materials needed for the succes of our experiments.


  1. 1.
    Perloff D, Sokolow M, Cowan R, et al. The prognostic value of ambulatory blood pressures. Jama. 1983;249(20):2792–8.CrossRefGoogle Scholar
  2. 2.
    Yang X-L, Wei H, Yan H, Xiao M, Sun X. Research progress of noninvasive continuous blood pressure measurements based on pulse wave velocity. Hangtian Yixue yu Yixue Gongcheng. 2011;24(6):467–72.Google Scholar
  3. 3.
    Obrist PA, Light KC, McCubbin JA, Stanford Hutcheson J, Lee J. Pulse transit time: relationship to blood pressure. Behav Res Methods. 1978;10(5):623–6.CrossRefGoogle Scholar
  4. 4.
    Omari T, Bereksi-Reguig F. An automatic wavelet denoising scheme for heart sounds. Int J Wavelets Multiresolut Inf Process. 2015;13(03):1550016.MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Sakamoto T, Kusukawa R, Maccanon DM, Luisada AA, Harvey I. Hemodynamic determinants of the amplitude of the first heart sound. Circ Res. 1965;16(1):45–57.CrossRefGoogle Scholar
  6. 6.
    Zhang X-Y, Zhang Y-T. Model-based analysis of effects of systolic blood pressure on frequency characteristics of the second heart sound. In: 28th annual international conference of the IEEE engineering in medicine and biology society, 2006. EMBS’06. IEEE; 2006. p. 2888–2891Google Scholar
  7. 7.
    Dastjerdi AE, Kachuee M, Shabany M. Non-invasive blood pressure estimation using phonocardiogram. In: 2017 IEEE international symposium on circuits and systems (ISCAS). IEEE; 2017. p. 1–4Google Scholar
  8. 8.
    Hsiao C-C, Horng J, Lee R-G, Lin R. Design and implementation of auscultation blood pressure measurement using vascular transit time and physiological parameters. In: 2017 IEEE international conference on systems, man, and cybernetics (SMC). IEEE; 2017. p. 2996–3001Google Scholar
  9. 9.
    Association for the Advancement of Medical Instrumentation et al. American national standard. manual, electronic or automated sphygmomanometers. ANSI/AAMI SP10-2002/A1, 2003.Google Scholar
  10. 10.
    Kario K, Hoshide S, Shimizu M, Yano Y, Eguchi K, Ishikawa J, Ishikawa S, Shimada K. Effect of dosing time of angiotensin II receptor blockade titrated by self-measured blood pressure recordings on cardiorenal protection in hypertensives: the japan morning surge-target organ protection (j-top) study. J Hypertens. 2010;28(7):1574–83.CrossRefGoogle Scholar
  11. 11.
    Yano Y, Hoshide S, Shimizu M, Eguchi K, Ishikawa J, Ishikawa S, Shimada K, Kario K. Association of home and ambulatory blood pressure changes with changes in cardiovascular biomarkers during antihypertensive treatment. Am J Hypertens. 2012;25(3):306–12.CrossRefGoogle Scholar
  12. 12.
    Nigam V, Priemer R. Simplicity based gating of heart sounds. In: 48th Midwest symposium on circuits and systems, 2005. IEEE; 2005. p. 1298–1301Google Scholar
  13. 13.
    Tahar O. Phonocardiogram signal analysis in the aim of measuring blood pressure. PhD thesis, Tlemcen univercity, Algeria, 2017Google Scholar
  14. 14.
    Fung P, Dumont G, Ries C, Mott C, Ansermino M. Continuous noninvasive blood pressure measurement by pulse transit time. In: The 26th annual international conference of the IEEE engineering in medicine and biology society, vol 1. IEEE; 2004. p. 738–741Google Scholar
  15. 15.
    Winter DA. Biomechanics and motor control of human movement. New York: Wiley; 2009.CrossRefGoogle Scholar
  16. 16.
    Tahar O, Nadia O, Redouane B, Nabil D, Fethi B-R. New parameter available in phonocardiogram for blood pressure estimation. In: International conference on bioinformatics and biomedical engineering. Springer; 2018. p. 301–310Google Scholar
  17. 17.
    Aizenberg I, Moraga C. Multilayer feedforward neural network based on multi-valued neurons (MLMVN) and a backpropagation learning algorithm. Soft Comput. 2007;11(2):169–83.CrossRefGoogle Scholar
  18. 18.
    Hirose Y, Yamashita K, Hijiya S. Back-propagation algorithm which varies the number of hidden units. Neural Netw. 1991;4(1):61–6.CrossRefGoogle Scholar
  19. 19.
    Tozawa M, Iseki K, Iseki C, Oshiro S, Yamazato M, Higashiuesato Y, Tomiyama N, Tana T, Ikemiya Y, Takishita S. Evidence for elevated pulse pressure in patients on chronic hemodialysis: a case-control study. Kidney Int. 2002;62(6):2195.CrossRefGoogle Scholar

Copyright information

© Korean Society of Medical and Biological Engineering 2019

Authors and Affiliations

  1. 1.Department of Biomedical EngineeringBoumerdes universityBoumerdesAlgeria
  2. 2.Department of Biomedical EngineeringTlemcen universityTlemcenAlgeria

Personalised recommendations