García-Nocetti FF, Solano GJ, Rubio AE, Moreno HE

Language: English
References: 12
Page: 12-19
PDF size: 371.13 Kb.

ABSTRACT

Doppler blood flow spectral estimation is a technique for non-invasive cardiovascular disease detection. Blood flow velocity and disturbance may be determined by measuring the spectral mean frequency and bandwidth respectively. Typical methods for spectral estimation utilize Fourier Transform-based algorithms to estimate the spectral response of a signal. This current practice suffers from poor frequency resolution when estimating non-stationary signals.The work presented here describes some alternative methods based on time-frequency distributions from a Cohen´s class point of view. Four distribution cases are evaluated: Wigner Ville, Choi Williams, Bessel and Born Jordan. A discrete distribution is formulated for each case and a criterion for determining the interaction between the spectral components of the signal is also given. Simplified discretised expressions for the implementation of distributions are formulated, these leading to a reduction of the computations realized when comparing to original definitions. A general parallel approach for the computation of the distributions is proposed, implemented and assessed using a parallel DSP-based system. Results are applied to the development of a real-time spectrum analyser for Doppler blood flow instrumentation.

REFERENCES

1. Fish P J, Non-Stationary Broadening in Pulsed Doppler Spectrum Measurements, Ultrasound in Medicine & Biology 17 (1991) 147-155.

2. Kay S M and Marple S L, Spectral Analysis a Modern Perspective, Proceedings of the IEEE 69 (1981) 1380-1419.

3. B. Boashash and P. Black, “An Efficient Real-Time Implementation of the Wigner-Ville Distribution”. IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. ASSP-35, pp. 1611-1618, 1987.

4. J. Cardoso, G. Ruano and P. Fish, “Nonstationary Broadening Reduction in Pulsed Doppler Spectrum Measurements Using Time-Frequency Estimators”, IEEE Transactions on Biomedical Engineering, vol. 43, pp. 1176-1186, 1996.

5. H. Choi and W. Williams, “Improved Time-Frequency Representation of Multicomponent Signals Using Exponential Kernels”, IEEE Transactions on Acoustics, Speech and Signal Processing, vol. 37, pp. 862-871, 1989.

6. L. Cohen, “Time-Frequency Distributions -A Review”, Proceedings of the IEEE, vol. 77, pp. 941-981, 1989.

7. L. Fan and D. Evans, “Extracting Instantaneous Mean Frequency Information from Doppler Signals Using the Wigner Distribution Function”, Ultrasound in Med. & Biol, vol. 20, pp. 429-443, 1994.

8. Z. Guo, L. Durand and H. Lee, “The Time-Frequency Distributions of Nonstationary Signals Based on a Bessel Kernel”, IEEE Transactions on Signal Processing, vol. 42, pp. 1700-1707, 1994.

9. W. Martin and P. Flandrin, “Wigner-Ville Spectral Analysis of Nonstationary Processes”, IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. ASSP-33, pp. 1461-1470, 1985.

10. Transtech DSP. ASP-P15 four node 21060 SHARC, User´s Manual,1999.

11. García Nocetti D.F., Solano J., Moreno E., Sotomayor A. “An Open High Performance System for Real-time Ultrasonic Imaging. Microprocessors and Microsystems (Elsevier) Vol. 23, Number 6, 357-363, 1999.

12. Solano J, García Nocetti D.F., Ruano M.G.. “High Performance Parallel-DSP Computing in Model-based Spectral Estimation”. Microprocessors and Microsystems (Elsevier) Vol. 23, Number 6, 337-344, 1999.