Blind System Identification of Noncoprime Multichannel Systems and Its Application to Noninvasive Cardiovascular Monitoring

[+] Author and Article Information
Yi Zhang

Guidant Corporation St. Paul, MN 55112

H. Harry Asada

Alex d’Arbeloff Laboratory for Information Systems and Technology, Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139

J. Dyn. Sys., Meas., Control 126(4), 834-847 (Mar 11, 2005) (14 pages) doi:10.1115/1.1852460 History: Received February 28, 2003; Revised January 03, 2004; Online March 11, 2005
Copyright © 2004 by ASME
Your Session has timed out. Please sign back in to continue.


Hori, C. et al., 1997, “Estimation of Aortic BP Wave Form From Noninvasive Radial Tonometry; Validation of FFT and ARX Methods,” Proceedings of the 19th Annual International Conference of the IEEE EMBS, Vol. 3, IEEE EMBS, Chicago, IL, pp. 1142–1145.
Abed-Meraim,  K., , 1997, “Blind System Identification,” Proc. IEEE, 85(12), pp. 1310–1332.
Tong,  L. , 1994, “Blind Identification and Equalization Based on Second-Order Statistics: A Time Domain Approach,” IEEE Trans. Signal Process., 40(2), pp. 340–349.
Gardner,  W. A., 1991, “A New Method of Channel Identification,” IEEE Trans. Commun., 39(6), pp. 813–817.
Xu,  G., , 1995, “A Least-Squares Approach to Blind Channel Identification,” IEEE Trans. Signal Process., 43(12), pp. 2982–2993.
Liu,  H., , 1996, “Recent Development in Blind Channel Equalization: From Cyclostationarity to Subspaces,” Signal Process., 50, pp. 83–99.
Tong,  L., and Perreau,  S., 1998, “Multichannel Blind Identification: From Subspace to Maximum Likelihood Methods,” Proc. IEEE, 86(10), pp. 1951–1968.
Gurelli,  M. I., and Nikias,  C. L., 1995, “EVAM: An Eigenvector-Based Algorithm for Multichannel Blind Deconvolution of Input Colored Signals,” IEEE Trans. Signal Process., 43(1), pp. 134–149.
Liu, H., Xu, G., and Tong, L., 1993, “A Deterministic Approach to Blind Equalization,” Conference Record of The Twenty-Seventh Asilomar Conference on Signals, Systems and Computers, Vol. 1, IEEE Signal Processing Society, Pacific Grove, CA, pp. 751–755.
Ljung, L., 1999, System Identification, Prentice-Hall, Upper Saddle River, NJ.
Moon, T. K., and Stirling, W. C., 2000, Mathematical Methods and Algorithms for Signal Processing, Prentice-Hall, Upper Saddle River, NJ.
Levine, V. S., ed., 1996, The Control Handbook, CRC Press, 1996.
Ozawa,  E. T. , 2001, “Numerical Simulation of Enhanced External Counterpulsation,” Ann. Biomed. Eng., 29(4), pp. 284–297.
Stergiopulos,  N., , 1995, “Evaluation of Methods for Estimation of Total Arterial Compliance,” Am. J. Physiol. Heart Circ. Physiol., 268(37), pp. H1540–H1548.


Grahic Jump Location
Schematic of a multichannel system
Grahic Jump Location
Anatomy of the systemic circulatory system
Grahic Jump Location
Topological structure of the systemic circulatory system analogous to a multichannel system
Grahic Jump Location
An all-zero system formulated to (a) solve the zero locations of a pole-zero system; (b) solve the pole locations of a pole-zero system
Grahic Jump Location
Multichannels having common dynamics (a) and ones with distinct channel dynamics driven by filtered input (b)
Grahic Jump Location
Formulation of the MBSI problem when common dynamics are present
Grahic Jump Location
Distinct channel dynamics Case 1: (a) Estimated versus real roots of channel 1; (b) estimated versus real roots of channel 2; (c) singular values of matrix Y in descending order; (d) estimated versus real impulse responses
Grahic Jump Location
Distinct channel dynamics Case 2: (a) Estimated versus real roots of channel 1; (b) estimated versus real roots of channel 2; (c) overlay of channels 1 and 2; (d) singular values of matrix Y in descending order
Grahic Jump Location
Aortic flow (a) and pressure signals (b) generated by distributed cardiovascular simulator and (c) the impulse responses from aortic flow to peripheral pressure
Grahic Jump Location
IIID test results for linearized cardiovascular system: (a) pole-zero location: true versus estimated; (b) three-channel outputs; and (c) estimated versus real inputs comparison when model structure is known
Grahic Jump Location
Comparison of estimated (recovered) input with real input when model structure is unknown
Grahic Jump Location
IIID implementation test result for a nonlinear, distributed system using cardiovascular simulator: comparison of estimated (recovered) input with real input



Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In