Skip to main content
SpringerLink
Log in

Comparison of Short-Time Fourier Transform and Eigenvector MUSIC Methods Using Discrete Wavelet Transform for Diagnosis of Atherosclerosis

  • Original Paper
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

In this paper, a more effective use of Doppler techniques is presented for the purpose of diagnosing atherosclerosis in its early stages using the carotid artery Doppler signals. The power spectral density (PSD) graphics are obtained by applying the short-time Fourier transform (STFT)-Welch and the Eigenvector MUSIC methods to the discrete wavelet transform (DWT) of Doppler signals. The PSDs for the fourth approximation component (A4) of both methods estimated that the patients with atherosclerosis in its early phase had lower maximum frequency components. On the other hand, the healthy subjects had higher maximum frequency components. The area under the curve (AUC), which belongs to the receiver operating characteristic (ROC) curve for the frequency level of the maximum PSDs of the A4 approximation obtained from the STFT modeling, is computed as 0.97. The AUC for the MUSIC modeling is computed as 0.996. The AUC belonging to the ROC curve for the higher maximum frequency component is computed as 0.87. The AUC belonging to the ROC curve for the test parameter of the frequency level of the maximum PSDs derived from the MUSIC modeling is determined to be 0.882. The results of this study clearly demonstrate that it is possible to distinguish between the healthy people and the patients with atherosclerosis by using the frequency level of the maximum PSDs for the A4 approximation. Furthermore, it is concluded that the power of Eigenvector-MUSIC method in terms of the resolution of the high frequencies is better than that of the STFT methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Hirai, T., Sasayama, S., Kawasaki, T., and Yagi, S., Stiffness of systemic arteries in patients with myocardial infarction. A noninvasive method to predict severity of coronary atherosclerosis. Circulation. 80:78–86, 1989.

    Google Scholar 

  2. Stefanadis, C., Stratos, C., Boudoulas, H., Kourouklis, C., and Toutouzas, P., Distensibility of the ascending aorta, comparison of invasive and non-invasive techniques in healthy men and in men with coronary artery disease. Eur. Heart J. 11:990–996, 1990.

    Google Scholar 

  3. Dart, A. M., Lacombe, F., Yeoh, J. K., Cameron, J. D., Jennings, G. L., Laufer, E., and Esmore, D. S., Aortic distensibility in patients with isolated hyper-cholesterolaemia, coronary artery disease, or cardiac transplant. Lancet. 338:270–273, 1991.

    Article  Google Scholar 

  4. Hoskins, P. R., McDicken, W. N., and Allan, P. L, Haemodynamics and blood flow. In: AllanP. L., DubbinsP. A., PozniakM. A., and McDickenW. N. (Eds.), Clinical Doppler UltrasoundChurchill Livingstone, London, pp. 27–38, 2000.

    Google Scholar 

  5. Schoen, F. J., and Cotran, R. S., Blood vessels Pathologic basis of disease, 6th edition. Saunders, Philadelphia, pp. 493–542, 1999.

    Google Scholar 

  6. Libley, P., Prevention and treatment of atherosclerosis. In: BraunwaldE., FauciA. S., KasperD. L., HauserS. L., LongoD. L., and JamesonJ. L. (Eds.), Harrison's principles of internal medicine, 6th edition. McGraw-Hill, London, pp. 1382–1385, 2001.

    Google Scholar 

  7. Evans, D., Doppler signal analysis. Ultrasound Med. Biol. 26:1S13–S15, 2000.

    Article  Google Scholar 

  8. Karabetsos, E., Papaodysseus, C., and Koutsouris, D., Design and development of a new ultrasonic doppler technique for estimation of the aggregation of red blood cells. Measurement. 24:207–215, 1998 Journal of the International Measurement Confederation, Elsevier Sc.

    Article  Google Scholar 

  9. Saini, V. D., Nanda, N. C., and Maulik, D., Basic principles of ultrasound and Doppler effect. Doppler echocardiography. Lea & Febiger, Philadelphia, 1993.

    Google Scholar 

  10. Keeton, P. I. J., and Schlindwein, F. S., Application of wavelets in Doppler ultrasound. Sensor Rev. 17:138–45, 1997.

    Article  Google Scholar 

  11. Sigel, B., A brief history of Doppler ultrasound in the diagnosis of peripheral vascular disease. Ultrasound Med. Biol. 24:2169–176, 1998.

    Article  Google Scholar 

  12. Evans, D. H., McDicken, W. N., Skidmore, R., and Woodcock, J. P., Doppler ultrasound: Physics, instrumentation and clinical applications. Wiley, Chichester, 1989.

    Google Scholar 

  13. Wright Isabel, A., and Gough Nigel, A. J., Artificial neural network analysis of common femoral artery Doppler shift signals: Classification of proximal disease. Ultrasound Med. Biol. 24:5735–743, 1999.

    Article  Google Scholar 

  14. Dirgenali, F., Kara, S., Erdoğan, N., and Okandan, M., Comparison of the Autoregressive modeling and fast Fourier transformation in demonstrating Doppler spectral waveform changes in the early phase of atherosclerosis. Comput. Biol. Med. 35:57–66, 2005.

    Article  Google Scholar 

  15. Van Asten, W. N., Beijneveld, W. J., Pieters, B. R. et al, Assessment of aorta iliac obstructive disease by Doppler spectrum analysis of blood flow velocities in the common femoral artery at rest and during reactive hyperemia. Surgery. 109:633–639, 1991.

    Google Scholar 

  16. Currie, I. C., Wilson, Y. G., Baird, R. N., and Lamont, P. M., Post occlusive hyperemic duplex scan: A new method of aorta iliac assessment. Br. J. Surg. 82:1226–1229, 1995.

    Article  Google Scholar 

  17. Dirgenali, F., and Kara, S., Recognition of early phase of atherosclerosis using principles component analysis and artificial neural networks from carotid artery Doppler signals. Expert Syst. Appl. 31:3643–651, 2006.

    Article  Google Scholar 

  18. Polat, K., Kara, S., Latifoğlu, F., & Güneş, S., A novel approach to resource allocation mechanism in artificial immune recognition system: Fuzzy resource allocation mechanism and application to diagnosis of atherosclerosis disease, Lecture Notes in Computer Science—LNCS, pp. 244–255, September, 2006.

  19. Latifoğlu, F., Şahan, S., Kara, S., & Güneş, S., Diagnosis of atherosclerosis from Carotid artery Doppler signals as a real-world medical application of artificial immune systems, Expert Systems with Applications. Vol 33/3 pp, November, 2007.

  20. Ceylan, M., Ceylan, R., Dirgenali, F., Kara, S., and Özbay, Y., Classification of carotid artery Doppler signals in the early phase of atherosclerosis using complex-valued artificial neural network. Comput. Biol. Med. 37:128–36, 2007.

    Article  Google Scholar 

  21. Polat, K., Kara, S., Latifoğlu, F., and Güneş, S., Pattern detection of atherosclerosis from carotid artery doppler signals using fuzzy weighted pre-processing and least square support vector machine (LSSVM). Ann. Biomed. Eng. 35:5724–732, 2007.

    Article  Google Scholar 

  22. Özbay, Y., Kara, S., Latifoğlu, F., Ceylan, R., and Ceylan, M., Complex-valued wavelet artificial neural network for Doppler signals classifying. Artif. Intell. Med. 40:143–156, 2007.

    Article  Google Scholar 

  23. Latifoğlu, F., Kodaz, H., Kara, S., and Güneş, S., Medical application of artificial immune recognition system (AIRS): Diagnosis of atherosclerosis from carotid artery Doppler signals. Comput. Biol. Med. 37:81092–1099, 2007.

    Article  Google Scholar 

  24. Gosling, R. G., and King, D. H., Continuous wave ultrasound as an alternative and complement to X-rays in vascular examination. In: RenemanR. S. (Ed.), Cardiovascular applications of ultrasound North-Holland, Amsterdam, pp. 266–282, 1974.

    Google Scholar 

  25. Planiol, T., and Pourcelot, L., Doppler effect study of the carotid circulation. Ultrasonics in medicine. Elsevier, New York, 1973.

    Google Scholar 

  26. Baird, R. N., Bird, D. R., Clifford, P. C., Lusby, R. J., Skidmore, R., and Woodcock, J. P., Upstream stenosis: Diagnosis by Doppler signals from the femoral artery. Arch. Surg. 115:111316–1322, 1980.

    Google Scholar 

  27. Macpherson, D. S., Evans, D. H., and Bell, P. R. F., Common femoral artery Doppler waveforms: A comparison of three methods of objective analysis with direct pressure measurements. Brit. J. Surg. 71:46–49, 1984.

    Article  Google Scholar 

  28. Kara, S., and Dirgenali, F., A system to diagnose atherosclerosis via wavelet transforms, principal component analysis and artificial neural networks. Expert Syst. Appl. 32:2632–640, 2007 March.

    Article  Google Scholar 

  29. Latifoğlu, F., Polat, K., Kara, S., and Güneş, S., Medical diagnosis of atherosclerosis from carotid artery doppler signals using principles component analysis (PCA), k-NN based weighting pre-processing and artificial immune recognition system (AIRS). J. Biomed. Informatics. 41:15–23, 2007.

    Article  Google Scholar 

  30. Kara, S., and Dirgenali, F., Detection of atherosclerosis using autoregressive modelling and principles component analysis to carotid artery Doppler signals. Bio-Med. Mater. Eng. 16/4:269–277, 2006.

    Google Scholar 

  31. Li, J. H., Peng, H., Du, Y. Y., Li, J. H., and Wang, W. H., Analysis of Doppler ultrasound blood flow signal basing on wavelet transform. J. Northeastern University (Nat Sci). 21:5487–489, 2000.

    Google Scholar 

  32. Cannon, S. R., Richards, K. L., and Rollwitz, W. T., Digital Fourier techniques in the diagnosis and quantification of aortic stenosis with pulse-Doppler echocardiography. J. Clin. Ultrasound. 10:101–107, 1982.

    Article  Google Scholar 

  33. Kalman, P. G., Johnston, K. W., Zuech, P., Kassam, M., and Poots, K., In vitro comparison of alternative methods for quantifying the severity of Doppler spectral broadening for the diagnosis of carotid arterial occlusive disease. Ultrasound Med. Biol. 11:3435–440, 1985.

    Article  Google Scholar 

  34. Guo, Z., Durand, L. G., and Lee, H. C., Comparison of time–frequency distribution techniques for analysis of simulated Doppler ultrasound signals of the femoral artery. IEEE Trans. Biomed. Eng. 41:4332–342, 1994.

    Article  Google Scholar 

  35. Chan, B. C. B., Chan, F. H. Y., Lam, F. K., Lui, P. W., and Poon, P. W. F., Fast detection of venous air embolism is Doppler heart sound using the wavelet transform. IEEE Trans. Biomed. Eng. 44:4237–245, 1997.

    Article  Google Scholar 

  36. Atıkson, P., Doppler ultrasound and its use in clinical measurement. Prentice-Hall, Englewood Cliffs, 1982.

    Google Scholar 

  37. Aydin, N., Marvasti, F., and Markus, H. S., Embolic Doppler ultrasound signal detection using discrete wavelet transform. IEEE Trans. Info. Technol. Biomed. 8:182–190, 2004.

    Article  Google Scholar 

  38. Proakis, J. G., Rader, C. M., Fuyun, L., and Chrysostomos, L., Advanced digital signal processing. Macmillan, New York, 1992.

    MATH  Google Scholar 

  39. Güler, I., Kara, S., Güler, N. F., and Kıymık, M. K., Application of autoregressive and fast Fourier transform spectral analysis to tricuspid and mitral valve stenosis. Comput. Methods Programs in Biomed. 49:29–36, 1996.

    Article  Google Scholar 

  40. Subasi, A., Erçelebi, E., Alkan, A., and Koklukaya, E., Comparison of subspace-based methods with AR parametric methods in epileptic seizure detection. Comput. Biol. Med. 36:195–208, 2004.

    Google Scholar 

  41. Porat, B., and Friedlander, B., Analysis of the asymptotic relative efficiency of the MUSIC algorithm. IEEE Transactions on Acoustics Speech and Signal Processing. 36:4532–544, 1988.

    Article  MATH  MathSciNet  Google Scholar 

  42. Swindlehurst, A. L., and Kailath, T., A performance analysis of subspace-based methods in the presence of model errors, Part II: Multidimensional algorithms. IEEE Trans. Signal Processing. 41:92882–2890, 1993.

    Article  MATH  Google Scholar 

  43. Hanley, J. A., and McNeil, B. J., The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology. 143:29–36, 1982.

    Google Scholar 

  44. Lee, W. C., and Hsiao, C. K., Alternative summary indices for the receiver operating characteristic curve. Epidemiology. 7:605–611, 1996.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Biomedical Engineering, Erciyes University, 38039, Kayseri, Turkey

    Fatma Latifoğlu

  2. Biomedical Engineering Institute, Fatih University, 34500, Istanbul, Turkey

    Sadık Kara

  3. Department of Electrical and Electronics Engineering, Fatih University, 34500, Istanbul, Turkey

    Erkan İmal

Authors
  1. Fatma Latifoğlu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sadık Kara
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Erkan İmal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sadık Kara.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Latifoğlu, F., Kara, S. & İmal, E. Comparison of Short-Time Fourier Transform and Eigenvector MUSIC Methods Using Discrete Wavelet Transform for Diagnosis of Atherosclerosis. J Med Syst 33, 189–197 (2009). https://doi.org/10.1007/s10916-008-9179-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10916-008-9179-z

Keywords

Search

Navigation