Time Frequency Analysis of Femoral Doppler Ultrasound Signals by AR Modelling

Abstract:

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.

• Periodical: Applied Mechanics and Materials (Volume 302)
• Pages: 319-325
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.302.319
• Online since: February 2013
• Authors: Latfaoui Mahieddine, Bereksi Reguig Fethi
• Keywords: AR Modelling, Femoral, Sonograms, STFT

References

[1] F. Dirgenali, S. Kara, ¸S. Okkesim , Estimation of wavelet and short-time Fourier transform sonograms of normal and diabetic subjects' electrogastrogram, Computers in Biology and Medicine 36, 2006, p.1289–1302.

DOI: 10.1016/j.compbiomed.2005.07.005

[2] J.M. Girault, Contribution of Signal Processing Techniques to the Analysis and Detection of embolic signals , Doctoral thesis, François Rabelais University, Tours, December (1999).

[3] Z. Guo, Time-frequency representation and pattern recognition of Doppler Blood Flow Signal for Stenosis Classification, PHD thesis, McGill University, Montreal, July (1993).

[4] K. Kaluzynski, Selection of spectral analysis method for assessment of velocity distribution based on the spectral distribution of ultrasonic Doppler signal, Med & Bio. Eng & Comput., 27, 1989, pp.463-469.

DOI: 10.1007/bf02441462

[5] K. Kaluzynski, T. Palko, Effect of method and parameters of spectral analysis on selected indices of simulated Doppler spectra, Med. Biol. Eng. Comput. 31, 1993, p.249–256.

DOI: 10.1007/bf02458044

[6] S. Kara, S. İçer, N. Erdogan, Spectral broadening of lower extremity venous Doppler signals using STFT and AR modelling, Digital Signal Processing 18, 2008, p.669–676.

DOI: 10.1016/j.dsp.2007.09.002

[7] P.I.J. Keeton, F.S. Schlindwein, Spectral broadening of clinical Doppler signals using FFT and autoregressive modelling, European Journal of Ultrasound 7, 1998, p.209–218.

DOI: 10.1016/s0929-8266(98)00032-9

[8] F.S. Schlindwein and D.H. Evans, Selection of order of autoregressive model for spectral analysis of Doppler ultrasound signals, Ultrasound Med Biol, 16(1), 1990, pp.81-91.

DOI: 10.1016/0301-5629(90)90089-u

[9] C. Simon, M. Schimmel, J.J. Dañobeitia, On the TT-Transform and Its Diagonal Elements, IEEE Trans. Signal Process., VOL. 56, NO. 11, November 2008, pp.5709-5713.

DOI: 10.1109/tsp.2008.929670

[10] P.J. Vaitkus, A comparative study and assessment of Doppler ultrasound spectral estimation techniques Part II : Methods and result, Ultrasound Med Biol, 14(8), 1988, pp.673-688.

DOI: 10.1016/0301-5629(88)90024-5

[11] Y. Zhang , L. Xu, J. Chen, H. Ma, X. Shi, Correction for broadening in Doppler blood flow spectrum estimated using wavelet transform, Medical Engineering & Physics 28, 2006, p.596–603.

DOI: 10.1016/j.medengphy.2005.09.002