<p>Ultrasonic Doppler signals are widely used in the detection of cardiovascular pathologies or the evaluation of the degree of stenosis in the femoral arteries. The presence of stenosis can be indicated by disturbing the blood flow in the femoral arteries, causing spectral broadening of the Doppler signal. To analyze these types of signals and determine stenosis index, a number of time-frequency methods have been developed, such as the short-time Fourier transform, the continuous wavelets transform, the wavelet packet transform, and the S-transform.</p>

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<p>Ultrasound Doppler signal processing is a necessity in several disciplines of medicine. The interpretation of these signals allows a good diagnosis of patients in order to detect certain diseases. In this work, we will use different non-parametric time-frequency techniques such as the Wigner-Ville distribution (WVD), the Pseudo-Wigner-Ville distribution (PWVD), the smoothed pseudo Wigner-Ville distribution (SPWVD), and the S-transform in order to analyze the Ultrasonic Doppler signal of the femoral arteries.</p>

In this study, we have compared the efficiency of the short time Fourier transform (STFT) and autoregressive modelling (AR) and autoregressive moving average (ARMA) of the femoral Doppler artery ultrasonic signals, in order to determine the spectral broadening index (SBI). Our aim is to detect the impact of the two modelling approaches on sonograms and of power spectral density- frequency diagrams obtained from femoral arterial Doppler Signals. The sonograms have been then used to compare the methods in terms of their frequency resolution and effects in determining the stenosis of femoral artery. In this paper we have used generated frequency envelopes from the Doppler spectrum to determine an index showing the degree of severity of stenosis cases. This index called broadening spectral index is calculated for various real cases.