Factors affecting Volterra kernel estimation: Emphasis on lung tissue viscoelasticity

Annals of Biomedical Engineering

Volumn 26, Issue 1, 1998, Pages 103-116

  Zhang, Qin ^a   Suki, Béla ^a   Westwick, David T ^a   Lutchen, Kenneth R ^a  

^a Boston University   (United States)

Author keywords

Lung model; Measurement noise; Memory length; Order of nonlinearity; Stress relaxation; Ventilatory wave form

Indexed keywords

ALGORITHMS; LEAST SQUARES APPROXIMATIONS; MATHEMATICAL MODELS; PHYSIOLOGICAL MODELS; STRESS RELAXATION; TISSUE; VISCOELASTICITY;

MEASUREMENT NOISE; MEMORY LENGTH; VENTILATORY WAVE FORM; VOLTERRA KERNEL ESTIMATION;

RESPIRATORY MECHANICS;

ALGORITHM; ARTICLE; LUNG; MATHEMATICAL MODEL; MEASUREMENT; MECHANICAL STRESS; MEMORY; SIGNAL PROCESSING; VISCOELASTICITY;

ALGORITHMS; ANIMALS; ARTIFACTS; BIAS (EPIDEMIOLOGY); DOGS; ELASTICITY; LEAST-SQUARES ANALYSIS; LUNG COMPLIANCE; NONLINEAR DYNAMICS; NORMAL DISTRIBUTION; NUMERICAL ANALYSIS, COMPUTER-ASSISTED; REPRODUCIBILITY OF RESULTS; RESPIRATORY MECHANICS; SIGNAL PROCESSING, COMPUTER-ASSISTED; STATISTICS, NONPARAMETRIC; VISCOSITY;

EID: 0031965221     PISSN: 00906964     EISSN: None     Source Type: Journal    
DOI: 10.1114/1.82     Document Type: Article

References (29)

1. Akaike, H. Fitting autoregressive models for prediction. Ann. Inst. Stat. Math. 21:243-347, 1969.
2. Akaike, H. A new look at the statistical model identification. IEEE Trans. Autom. Control. AC-19(6):716-723, 1974.
3. Barahona, M., and C. Poon. Detection of nonlinear dynamics in short, noisy time series. Nature (London) 381(16):215-217, 1996.
4. Barnas, G. M., D. Stamenović, and K. R. Lutchen. Lung and chest wall impedances in the dog in normal range of breathing: Effects of pulmonary edema. J, Appl Physiol. 73:1040-1046, 1992.
5. Courellis, S. H., and V. Z. Marmarelis. Wiener analysis of Hodgkin-Huxley equation. In: Advanced Methods of Physiological System Modeling, Vol. 2, edited by V. Z. Marmarelis. New York: Plenum, 1989, pp. 273-297.
6. French, A. S., and M. J. Korenberg. A nonlinear cascade model for action potential encoding in an insect sensory neuron. Biophys. J. 55:655-661, 1989.
7. French, A. S., M. J. Korenberg, M. Jarvilehto, E. Kouyalainen, M. Juusolla. and M. Weckström The dynamic nonlinear behavior of fly photoreceptor evoked by a wide range of light intensities. Biophys. J. 65:832-839, 1993.
8. French. A. S., and V. Z. Marmarelis. Nonlinear neural mode analysis of action potential encoding in the cockroach tactile spine neuron. Biol. Cybern. 73:425-430, 1995.
9. Giordano, A. A., and F. M. Hsu. Least square estimation with applications to digital signal processing. New York: Wiley, 1985, p. 412.
10. Hung, G. K., and L. W. Stark. The interpretation of kernels - an overview. Ann. Biomed. Eng. 19:509-519, 1991.
11. Korenberg, M. J. Fast orthogonal algorithms for nonlinear system identification and time-series analysis. In: Advanced Methods of Physiological System Modeling, Vol. 2, edited by V. Z. Marmarelis. New York: Plenum. 1989, pp. 165-177.
12. Korenberg, M. J. Parallel cascade identification and kernel estimation for nonlinear systems. Ann. Biomed. Eng. 19(4):429-455, 1991.
13. Korenberg, M. J., and I. W. Hunter. The identification of nonlinear biological systems: Volterra kernel approaches. Ann. Biomed. Eng. 24(4):250-268, 1996.
14. Krenz, W., and L. Stark. Interpretation of functional series expansions. Ann. Biomed. Eng. 19:485-508, 1991.
15. Lee, Y. W., and M. Schetzen. Measurement of the Wiener kernels of nonlinear system by cross-correlation. Int. J. Control 2:237-254, 1965.
16. Ljung, L. System Identification: Theory for the Users. Englewood Cliffs, NJ: Prentice-Hall, 1987, pp. 419-421.
17. Lutchen, K. R., B. Suki, Q. Zhang, F. Peták. B. Daróczy. and Z. Hantos. Airway and tissue mechanics during physiological breathing and bronchoconstriction in dogs. J. Appl. Physiol. 77(1):373-385, 1994.
18. Lutchen, K. R., K. Yang, D. W. Kaczka, and B. Suki. Optimal ventilator waveform for estimating low frequency mechanical impedance in healthy and diseased subjects. J. Appl. Physiol. 75(1):478-488, 1993.
19. Marmarelis, V. Z. Identification of nonlinear biological systems using Laguerre expansions of kernels. Ann. Biomed. Eng. 21(6):573-589, 1993.
20. Marmarelis, P. Z., and V. Z. Marmarelis. Analysis of Physiological Systems: The White-noise Approach. New York: Plenum, 1978, p. 487.
21. Schetzen, M. The Volterra and Wiener theories of nonlinear systems. Malabar, FL: Krieger, 1989, p. 573.
22. Stein, R. B., and R. E. Kearney. Nonlinear behavior of muscle reflexes at the human ankle joint. J. Neurophysiol. 73(1):65-72, 1995.
23. Suki, B., A. Barabási, and K. R. Lutchen. Lung tissue viscoelasticity: A mathematical framework and its molecular basis. J. Appl. Physiol. 76(6):2749-2759, 1994.
24. Suki, B., and K. R. Lutchen. Pseudorandom signals to estimate apparent transfer and coherence functions of nonlinear systems: Applications to respiratory mechanics. IEEE Trans. Biomed. Eng. 39(11):1142-1151, 1992.
25. Suki, B., Q. Zhang, and K. R. Lutchen. Relationship between frequency and amplitude dependence in the lung: A nonlinear block-structured modeling approach. J. Appl. Physiol. 79(2):660-671, 1995.
26. Victor, J. D. The fractal dimension of a test signal: Implications for system identification procedures. Brol. Cybern. 57:421-426, 1987.
27. Victor, J., and R. Shapley. A method of nonlinear analysis in the frequency domain. Biophys. J. 29:459-484, 1980.
28. Watanabe, A., and L. Stark. Kernel method for nonlinear analysis: Identification of a biological control system. Math. Biosci. 27:99-108, 1975.
29. Westwick, D. T., and R. E. Kearney. Identification of multiple-input nonlinear systems using non-white test signals. In: Advanced Methods of Physiological System Modeling, Vol. 3. edited by V. Z. Marmarelis. New York: Plenum, 1994, pp. 163-178.