ON THE USEFULNESS OF SINGULAR VALUE DECOMPOSITION - ARMA MODELS IN DOPPLER ULTRASOUND

Recently, singular value decomposition (SVD) in connection with autoregressive moving average models of order p and q, ARMA (p,q), has been recommended as a particularly good approach towards parametric spectral estimation in medical Doppler ultrasound. Compared to the traditional FFT based spectral estimators, the advantages of the SVD-ARMA algorithm include a reduction in bias and variance and an inherent capability for model order selection. In the paper the SVD-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 were 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.