Equivalence between "Feeling the pulse" on the human wrist and the pulse pressure wave at fingertip

International Journal of Neural Systems

Volumn 15, Issue 4, 2005, Pages 277-286

  Zhao, Y ^a   Small, Michael ^a  

^a HONG KONG POLYTECHNIC UNIVERSITY   (Hong Kong)

Author keywords

Description length; Electrocardiogram; Neural networks; Pulse pressure; Surrogate data method

Indexed keywords

ARTICLE; ARTIFICIAL NEURAL NETWORK; BIOLOGICAL MODEL; BLOOD PRESSURE; BLOOD PRESSURE MEASUREMENT; COMPARATIVE STUDY; FINGER; HUMAN; METHODOLOGY; PHYSIOLOGY; PULSE RATE; WRIST;

BLOOD PRESSURE; BLOOD PRESSURE DETERMINATION; FINGERS; HUMANS; MODELS, BIOLOGICAL; NEURAL NETWORKS (COMPUTER); PULSE; WRIST;

EID: 33644617046     PISSN: 01290657     EISSN: None     Source Type: Journal    
DOI: 10.1142/S0129065705000232     Document Type: Article

References (17)

1. Y. Zhao and M. Small, Deterministic propagation of blood pressure waveform from human wrists to fingertips, Lecture Notes in Computer Science 3177 (2004) 142-147.
2. E. A. Wan, Time Series Prediction: Forecasting the Future and Understanding the Past (Addison-Wesley, Reading, MA, 1993), pp. 195-217.
3. Neural Network Toolbox User's Guide, 4th Ver. (The Math Works, inc., Natick, MA, 2000), Chap. 5, pp. 52-57.
4. J. Rassanen, Stochastic Complexity in Statistical Inquiry (World Scientific, Singapore, 1989).
5. K. Judd and A. Mees, On selecting models for nonlinear time series, Physica D 82 (1995) 426-444.
6. M. Small and C. K. Tse, Minimum description length neural networks for time series prediction, Physical Review E 66 (2002) 066701.
7. A. Galka, Topics in Nonlinear Time Series Analysis with Implications for EEG Analysis (World Scientific, Singapore, 2000), p. 222.
8. J. Theiler, S. Eubank, A. Longtin, B. Galdrikian and J. D. Farmer, Testing for nonlinearly in time series: The method of surrogate data, Phys. D 58 (1992) 77-94.
9. G. A. Barnard, Discussion on: The spectral analysis of point processes (by M. S. Bartlett), J. Royal Stat. Soc. B 25 (1963) 294.
10. M. Small and C.K. Tse, Applying the method of surrogate data to cyclic time series, Phys. D 164 (2002) 182-202.
11. D. Yu and M. Small et al., Efficient implementation of the Gaussian kernel algorithm in estimating invariants and noise level from noisy time series data, Phys. Rev. E 61 (2000) 3750.
12. M. B. Kennel, R. Brown and H. D. I. Abarbanel, Determining embedding dimension for phase-space reconstruction using a geometrical construction, Phys. Rev. A 45 (1992) 3403.
13. O. E. Roessler, Phys. Lett. A57 (1976) 397.
14. K. Ikeda, Opt. Comm. 30 (1979) 257.
15. A. S. Weigend and N. A. Gershenfeld (eds.), Time Series Prediction: Forecasting the Future and Understanding the Past (Addison-Wesley, Reading, MA, 1994).
16. M. Small and D. J. Yu et al., Uncovering non-linear structure in human EGG recordings, Chaos, Solitons and Fractals 13 (2002) 1755-1762.
17. M. T. Hagan and M. Menhaj, Training feedforward networks with the Marquardt algorithm, IEEE Trans. Neural Networks 5(6) (1994) 989-993.