Journal ArticleDOI
On the usefulness of singular value decomposition-ARMA models in Doppler ultrasound
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TLDR
The singular value decomposition (SVD) autoregressive moving average, (ARMA) procedure is applied to computer-generated synthetic Doppler signals as well as in-vivo Dopplers data recorded in the carotid artery, and it is found that no single set of model orders was capable of producing consistent spectral estimates throughout the cardiac cycle.Abstract:
The singular value decomposition (SVD) autoregressive moving average, (ARMA) procedure is applied to computer-generated synthetic Doppler signals as well as in-vivo Doppler data recorded in the carotid artery. Two essential algorithmic parameters (the initially proposed model order and the number of overdetermined equations used) prove difficult to choose. The resulting spectra are very dependent on these two parameters. For the simulated data models orders of (3, 3) provide good results. However, when applying the SVD-ARMA algorithm to in-vivo Doppler signals no single set of model orders was capable of producing consistent spectral estimates throughout the cardiac cycle. Altering the model orders also necessitates the selection of new algorithmic parameters. Hence, the SVD-ARMA approach cannot be considered suitable as a spectral estimation technique, for real-time Doppler ultrasound systems. >read more
Citations
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High resolution ultrasonic backscatter coefficient estimation based on autoregressive spectral estimation using Burg's algorithm
TL;DR: An autoregressive method for spectral estimation was applied toward the task of estimating ultrasonic backscatter coefficients from small volumes of tissue, offering promise for enhanced spatial resolution and accuracy in ultrasonic tissue characterization and nondestructive evaluation of materials.
References
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Steven Kay,S.L. Marple +1 more
TL;DR: In this paper, a summary of many of the new techniques developed in the last two decades for spectrum analysis of discrete time series is presented, including classical periodogram, classical Blackman-Tukey, autoregressive (maximum entropy), moving average, autotegressive-moving average, maximum likelihood, Prony, and Pisarenko methods.
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Spectral estimation: An overdetermined rational model equation approach
TL;DR: In this paper, it is shown that by taking this overdetermined parametric evaluation approach, a reduction in data-induced model parameter hypersensitivity is obtained, and a corresponding improvement in modeling performance results.
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A comparative study and assessment of Doppler ultrasound spectral estimation techniques. Part II: Methods and results.
TL;DR: Results indicate that both the AR(Yule-Walker) and ARMA(singular value decomposition) models of orders (8) and (4,4), respectively, show good agreement with the theoretical spectrum, and yield estimates with variances considerably less than the Fast Fourier Transform (FFT).
Journal ArticleDOI
A comparative study and assessment of Doppler ultrasound spectral estimation techniques. Part I: Estimation methods.
TL;DR: In this article, the authors consider spectral estimation methods as a problem of fitting an assumed model to the Doppler signal, where the models described assume that the signal is stationary and a short enough time window interval can be chosen over which the signal can be considered stationary.