Novel wavelet neural network algorithm for continuous and noninvasive dynamic estimation of blood pressure from photoplethysmography


This paper proposes a novel wavelet neural network algorithm for the continuous and noninvasive dynamic estimation of blood pressure (BP). Unlike prior algorithms, the proposed algorithm capitalizes on the correlation between photoplethysmography (PPG) and BP. Complete BP waveforms are reconstructed based on PPG signals to extract systolic blood pressure (SBP) and diastolic blood pressure (DBP). To improve the robustness, Daubechies wavelet is implemented as the hidden layer node function for the neural network. An optimized neural network structure is proposed to reduce the computational complexity. Further, this paper investigates an inhomogeneous resilient backpropagation (IRBP) algorithm to calculate the weight of hidden layer nodes. The IRBP improves the convergence speed and reconstruction accuracy. Multiparameter intelligent monitoring in Intensive Care (MIMIC) databases, which contain a variety of physiological parameters captured from patient monitors, are used to validate this algorithm. The standard deviation σ between reconstructed and actual BP signals is 4.4797 mmHg, which satisfies the American National Standards of the Association for the Advancement of Medical Instrumentation. The reconstructed BP waveform can be used to extract the SBP and DBP, whose standard deviations σ are 2.91 mmHg and 2.41 mmHg respectively.

This is a preview of subscription content, access via your institution.


  1. 1

    Zhang X, PeiWH, Huang B J, et al. A low-noise fully-differential CMOS preamplifier for neural recording applications. Sci China Inf Sci, 2012, 55: 441–452

    MathSciNet  Article  Google Scholar 

  2. 2

    Zhang X, Pei W H, Huang B J, et al. Implantable CMOS neurostimulus chip for visual prosthesis. Sci China Inf Sci, 2011, 54: 898–908

    MathSciNet  Article  Google Scholar 

  3. 3

    Xu Z, Ming L, Bo W, et al. A wide measurement range and fast update rate integrated interface for capacitive sensors array. IEEE Trans Circuits Syst I: Regular Papers, 2014, 61: 2–11

    Article  Google Scholar 

  4. 4

    Wang Y, Zhang X, Liu M, et al. An implantable sacral nerve root recording and stimulation system for micturition function restoration. IEICE Trans Inform Syst, 2014, 97-D: 2790–2801

    Article  Google Scholar 

  5. 5

    Hu X H, Zhang X, Liu M, et al. A flexible capacitive tactile sensor array with micro structure for robotic application. Sci China Inf Sci, 2014, 57: 120204(6)

    Google Scholar 

  6. 6

    Ye Y L, Sheu P C-Y, Zeng J Z, et al. An efficient semi-blind source extraction algorithm and its applications to biomedical signal extraction. Sci China Ser F-Inf Sci, 2009, 52: 1863–1874

    Article  MATH  Google Scholar 

  7. 7

    Wang G, Rao N N, Zhang Y, et al. Atrial fibrillatory signal estimation using blind source extraction algorithm based on high-order statistics. Sci China Ser F-Inf Sci, 2008, 51: 1572–1584

    Article  MATH  Google Scholar 

  8. 8

    An J, Lee J H, Ahn C W. An efficient GP approach to recognizing cognitive tasks from fNIRS neural signals. Sci China Inf Sci, 2013, 56: 109201(7)

    MathSciNet  Google Scholar 

  9. 9

    Li P, Liu M, Zhang X, et al. A low-complexity ECG processing algorithm based on the Haar wavelet transform for portable health-care devices. Sci China Inf Sci, 2014, 57: 122303(14)

    Google Scholar 

  10. 10

    Zorn E A, Wilson M B, Angel J J, et al. Validation of an automated arterial tonometry monitor using association for the advancement of medical instrumentation standards. Blood Pressure Monitor, 1997, 2: 185–188

    Google Scholar 

  11. 11

    Miyauchi Y, Koyama S, Ishizawa H. Basic experiment of bloodpressure measurement which uses FBG sensors. In: Proceedings of the IEEE International Conference on Instrumentation and Measurement Technology, Minneapolis, 2013. 1767–1770

    Google Scholar 

  12. 12

    Newlin D B. Relationships of pulse transmission times to pre-ejection period and blood pressure. Phychophysiology, 1981, 18: 316–321

    Article  Google Scholar 

  13. 13

    Lane J D, Greenstadt L, Shapiro D. Pulse transit time and blood pressure: an intensive analysis. Phychophysiology, 1983, 20: 45–49

    Article  Google Scholar 

  14. 14

    Xiaochuan H, Goubran R A, Liu X P. Evaluation of the correlation between blood pressure and pulse transit time. In: Proceedings of the IEEE International Conference on Medical Measurements and Applications Proceedings, Gatineau, 2013. 17–20

    Google Scholar 

  15. 15

    Chen Y, Wen C, Tao G, et al. Continuous and noninvasive measurement of systolic and diastolic blood pressure by one mathematical model with the same model parameters and two separate pulse wave velocities. Ann Biomed Eng, 2012, 40: 871–882

    Article  Google Scholar 

  16. 16

    Li Y J, Wang Z L, Zhang L, et al. Characters available in photoplethysmogram for blood pressure estimation: beyond the pulse transit time. Australas Phys Eng Sci, 2014, 37: 367–376

    Article  Google Scholar 

  17. 17

    Teng X F, Zhang Y T. Continuous and noninvasive estimation of arterial blood pressure using a photoplethysmographic approach. In: Proceedings of the IEEE International Conference on Medicine and Biology Society, Cancun, 2003. 3153–3156

    Google Scholar 

  18. 18

    Yoon Y, Yoon G. Nonconstrained blood pressure measurement by photoplethysmography. J Opt Soc Korea, 2006, 10: 91–95

    Article  Google Scholar 

  19. 19

    Fortino G, Giampà V. PPG-based methods for non invasive and continuous BP measurement: an overview and development issues in body sensor networks. In: Proceedings of the IEEE International Conference on Medical Measur, Ottawa, 2010. 10–13

    Google Scholar 

  20. 20

    Kurylyak Y, Lamonaca F, Grimaldi D. A neural network-based method for continuous blood pressure estimation from a PPG signal. In: Proceedings of the IEEE International Conference on Instrumentation and Measurement Technology, Minneapolis, 2013. 280–283

    Google Scholar 

  21. 21

    Boggess A, Narcowich F J. A First Course in Wavelets with Fourier Analysis. Hoboken: Wiley, 2001

    MATH  Google Scholar 

  22. 22

    Qinghua Z. Using wavelet network in nonparametric estimation. IEEE Trans Neural Networks, 1997, 8: 227–236

    Article  Google Scholar 

  23. 23

    George B M, Roger G M. A database to support development and evaluation of intelligent intensive care monitoring. In: Proceedings of the IEEE International Conference on Computers in Cardiology, Indianapolis, 1996. 657–660

    Google Scholar 

  24. 24

    Hornik K. Multilayer feedforward networks are universal approximators. Neural Netw, 1989, 2: 183–192

    Article  Google Scholar 

  25. 25

    Barron A. Universal approximation bounds for superpositions of a sigmoidal function. IEEE Trans Inform Theory, 1993, 39: 930–945

    MathSciNet  Article  MATH  Google Scholar 

  26. 26

    Poggio T, Girosi F. Networks for approximation and learning. Proc IEEE, 1990, 78: 1481–1497

    Article  MATH  Google Scholar 

  27. 27

    Zhang Q, Benveniste A. Wavelet networks. IEEE Trans Neural Netw, 1992, 3: 889–898

    MathSciNet  Article  Google Scholar 

  28. 28

    Zhang J, Walter G G, Miao Y, et al. Wavelet neural networks for function learning. IEEE Trans Signal Process, 1995, 43: 1485–1497

    Article  Google Scholar 

  29. 29

    Igel C, Husken M. Empirical evaluation of the improved Rprop learning algorithms. Neurocomputing, 2003, 50: 105–123

    Article  MATH  Google Scholar 

  30. 30

    Rusiecki A. Robust learning algorithm based on iterative least median of squares. Neural Process Lett, 2012, 36: 145–160

    Article  Google Scholar 

  31. 31

    American National Standard. Electronic or Automated Sphygmomanometers. ANSI/AAMI SP10, Association for the Advancement of Medical Instrumentation, Arlington, 1992

  32. 32

    Younhee C, Qiao Z, Seokbum K. Noninvasive cuffless blood pressure estimation using pulse transit time and Hilbert-Huang transform instrumentation. Comput Electr Eng, 2013, 39: 103–111

    Article  Google Scholar 

Download references

Author information



Corresponding authors

Correspondence to Ming Liu or Xu Zhang.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Li, P., Liu, M., Zhang, X. et al. Novel wavelet neural network algorithm for continuous and noninvasive dynamic estimation of blood pressure from photoplethysmography. Sci. China Inf. Sci. 59, 042405 (2016).

Download citation


  • photoplethysmogram (PPG)
  • blood pressure (BP)
  • Daubechies wavelet
  • neural network
  • inhomogeneous resilient backpropagation (IRBP)