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

Abstract

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.

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References

  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

  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

  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

  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

  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)

  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

  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

  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)

  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)

  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

  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

  12. 12

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

  13. 13

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

  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

  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

  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

  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

  18. 18

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

  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

  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

  21. 21

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

  22. 22

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

  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

  24. 24

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

  25. 25

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

  26. 26

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

  27. 27

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

  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

  29. 29

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

  30. 30

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

  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

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Correspondence to Ming Liu or Xu Zhang.

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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). https://doi.org/10.1007/s11432-015-5400-0

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Keywords

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