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Deterministic Propagation of Blood Pressure Waveform from Human Wrists to Fingertips

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Intelligent Data Engineering and Automated Learning – IDEAL 2004 (IDEAL 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3177))

Abstract

Feeling the pulse on the wrist is one of the most important diagnostic methods in traditional Chinese medicine (TCM). In this paper we test whether there is any difference between feeling the pulse on the wrist or at any other part of the body, such as at the fingertips? To do this we employ the optimal neural networks estimated by description length to model blood pressure propagation from the wrist to the fingertip, and then apply the method of surrogate data to the residuals of this model. Our result indicates that for healthy subjects measuring pulse waveform at the fingertip is equivalent to feeling pulse on the lateral artery (wrist).

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References

  1. Wan, E.A.: Time series prediction: Forecasting the future and understanding the past. studies in the sciences of complexity, vol. XV, pp. 195–217. Addison-Wesley, MA (1993)

    Google Scholar 

  2. Neural Network Toolbox User’s Guide, 4th Ver., the Math Works, Inc. 5, 52–57 (2000)

    Google Scholar 

  3. Rassanen, J.: Stochastic complexity in statistical inquiry. World Scientific, Singapore (1989)

    Google Scholar 

  4. Zhao, Y., Small, M.: How many neurons? An information theoretic method for selecting model size. IEEE Transactions on Neural Networks (submitted for publication)

    Google Scholar 

  5. Galka, A.: Topics in Nonlinear Time Series Analysis with Implications for EEG Analysis. World Scientific, Singapore (2000)

    Book  MATH  Google Scholar 

  6. Small, M., Tse, C.K.: Detecting determinism in time series: the method of surrogate data. IEEE Trans. on Circuits and System-I 50, 663–672 (2003)

    Article  MathSciNet  Google Scholar 

  7. Yu, D., Small, M., Harrison, R.G., Diks, C.: Efficient implementation of the Gaussian kernel algorithm in estimating invariants and noise level from noisy time series data. Phys. Rev. E 61 (2000)

    Google Scholar 

  8. Kennel, M.B., Brown, R., Abarbanel, H.D.I.: Determining embedding dimension for phase-space reconstruction using a geometrical construction. Phys. Rev. A 45, 3403 (1992)

    Article  Google Scholar 

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© 2004 Springer-Verlag Berlin Heidelberg

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Zhao, Y., Small, M. (2004). Deterministic Propagation of Blood Pressure Waveform from Human Wrists to Fingertips. In: Yang, Z.R., Yin, H., Everson, R.M. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2004. IDEAL 2004. Lecture Notes in Computer Science, vol 3177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28651-6_20

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  • DOI: https://doi.org/10.1007/978-3-540-28651-6_20

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22881-3

  • Online ISBN: 978-3-540-28651-6

  • eBook Packages: Springer Book Archive

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