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Pattern Detection of Atherosclerosis from Carotid Artery Doppler Signals using Fuzzy Weighted Pre-Processing and Least Square Support Vector Machine (LSSVM)

Annals of Biomedical Engineering volume 35, pages 724–732 (2007)Cite this article

Abstract

Carotid Artery Doppler Signals were recorded from 114 subjects, 60 of whom had Atherosclerosis disease while the rest were healthy controls. Diagnosis of Atherosclerosis from Carotid Artery Doppler Signals was conducted using Fuzzy weighted pre-processing and Least Square Support Vector Machine (LSSVM). First, in order to determine the LSSVM inputs, spectral analysis of Carotid Artery Doppler Signals was performed via Autoregressive (AR) modeling. Then, fuzzy weighted pre-processing based is proposed expert system, applied to inputs obtained from spectral analysis of Carotid Artery Doppler Signals. LSSVM was used to detect Atherosclerosis from Carotid Artery Doppler Signals. All data set were obtained from Carotid Artery Doppler Signals of healthy subjects and subjects suffering from Atherosclerosis disease. The employed expert system has achieved 100% classification accuracy using a 10-fold Cross Validation (CV) method.

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References

  1. Akaike H., (1974), A new look at the statistical model identification. IEEE Trans. Automat. Control AC-19:716–723

    Article  Google Scholar 

  2. Baird R. N., Bird D. R., Clifford P. C., Lusby R. J., Skidmore R., Woodcock J. P. (1980) Upstream stenosis: diagnosis by Doppler signals from the femoral artery. Arch. Surg. 115(11):1316 –1322

    PubMed  CAS  Google Scholar 

  3. Cheung, N. 2001. Machine learning techniques for medical analysis. BSc thesis, School of Information Technology and Electrical Engineering, University of Queenland

  4. Currie I. C., Wilson Y. G., Baird R. N., Lamont P. M. (1995) Postocclusive hyperaemic duplex scan: a new method of aortoiliac assessment. Br. J. Surg. 82:1226–1229

    PubMed  Article  CAS  Google Scholar 

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

    Google Scholar 

  6. Dean Forbes A. (1995). Classification-algorithm evaluation: five performance measures based onconfusion matrices. J. Clin. Monitor. Comput. 11(3):189–206

    Article  Google Scholar 

  7. Delen D., Walker G., Kadam A., (2004). Predicting breast cancer survivability: a comparison of three data mining methods. Artific. Intell. Med. 34(2):113–127

    Article  Google Scholar 

  8. Evans D. (2000) Doppler signal analysis. Ultrasound Med. Biol. 26(Supplement 1):S13–S15

    PubMed  Article  Google Scholar 

  9. Evans, D. H., W. N. McDicken, R. Skidmore, and J. P. Woodcock. Doppler Ultrasound: Physics, Instrumentation and Clinical Applications. Wiley, Chichester, 1989

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

    Google Scholar 

  11. Hirai, T., S. Sasayama, T. Kawasaki, and S. Yagi. 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 

  12. Jeff Schneider’s Home Page, http://www.cs.cmu.edu/∼schneide/tut5/node42.html (last accessed: May, 30, 2006)

  13. Kaluzynski K. (1989) Order selection in Doppler blood flow signal spectral analysis using autoregressive modeling. Med. Biol. Eng. Comput. 27:89–92

    PubMed  Article  CAS  Google Scholar 

  14. Kara, S. A study of mitral and tricuspid valve blood flows by autoregressive spectral analysis method and Doppler unit. Thesis of Doctorate, Institute of Science of Erciyes University Press, 1995

  15. Kay S. M., Marple S. L. (1981) Spectrum analysis – a modern perspective. Proc. IEEE, 69(11):1380–1419

    Article  Google Scholar 

  16. Macpherson D. S., Evans D. H., Bell P. R. F. (1984) Common femoral artery Doppler waveforms: a comparison of three methods of objective analysis with direct pressure measurements. Br. J. Surg. 71:46–49

    PubMed  Article  CAS  Google Scholar 

  17. Muller M., Ciccotti P., Reiche W., Hagen T. (2001) Comparison of color flow Doppler scanning, power Doppler scanning, and frequency shift for assessment of carotid artery stenosis. J. Vasc. Surg. 34:1090–1095

    PubMed  Article  CAS  Google Scholar 

  18. Planiol T., Pourcelot L. (1973), Doppler Effect Study of the Carotid Circulation. Ultrasonics in Medicine. Elsevier, New York

    Google Scholar 

  19. Polat K., Şahan S., Güneş S. (2006). A new method to medical diagnosis: artificial immune recognition system (AIRS) with fuzzy weighted pre-processing and application to ECG arrhythmia. Expert Syst. Appl. 31(2):264–269

    Article  Google Scholar 

  20. Saini, V. D., N. C. Nanda, and D. Maulik. Basic principles of ultrasound and Doppler effect. In: Doppler Echocardiography, edited by N. C. Nanda. London: Lea & Febiger, 1993

  21. Schlindwein F.S., Evans D.H., (1990), Selection of the order of autoregressive models for spectral analysis of Doppler ultrasound signals. Ultrasound Med. Biol. 16(1): 81–91

    PubMed  Article  CAS  Google Scholar 

  22. Sigel B. (1998) A brief history of Doppler ultrasound in the diagnosis of peripheral vascular disease. Ultrasound Med. Biol. 24:169–176

    PubMed  Article  CAS  Google Scholar 

  23. Stefanadis, C., C. Stratos, H. Boudoulas, C. Kourouklis, and P. Toutouzas. 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 

  24. Suykens J. A. K., Vandewalle J. (1999) Least squares support vector machine classifiers. Neural Process. Lett. 9(3):293–300

    Article  Google Scholar 

  25. Tsujinishi, D., and S. Abe. Fuzzy least squares support vector machines for multi-class problems. Neural Netw. 16:785–792, 2003

    Google Scholar 

  26. Vaitkus P. J., Cobbold R. S. C., Johnston K. W. (1988) A comparative study and assessment of Doppler ultrasound spectral estimation techniques part II: methods and results. Ultrasound Med. Biol. 14:673–688

    PubMed  Article  CAS  Google Scholar 

  27. Van Asten W. N., Beijneveld W. J., Pieters B. R., et al. (1991) Assessment of aortoiliac 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

    PubMed  Google Scholar 

  28. Vapnik V. (1995) The Nature of Statistical Learning Theory. Springer, New York

    Google Scholar 

  29. Wang X., Paliwal K. K. (2003). Feature extraction and dimensionality reduction algorithms and their applications in vowel recognition. Pattern Recognit. 36:2429– 2439

    Article  Google Scholar 

  30. Yeh C. K., Li P. C. (2002) Doppler Angle estimation of pulsatile flows using AR modeling. Ultrason. Imaging 24:135–146

    Google Scholar 

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Acknowledgment

This study is supported by the Scientific Research Projects of Selcuk University (project no. 05401069).

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Affiliations

  1. Department of Electrical & Electronics Engineering, Selcuk University, 42075, Konya, Turkey

    Kemal Polat & Salih Güneş

  2. Department of Electronics Engineering, Erciyes University, 38039, Kayseri, Turkey

    Sadık Kara & Fatma Latifoğlu

Authors
  1. Kemal Polat
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  2. Sadık Kara
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  3. Fatma Latifoğlu
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  4. Salih Güneş
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Correspondence to Sadık Kara.

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Polat, K., Kara, S., Latifoğlu, F. et al. 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, 724–732 (2007). https://doi.org/10.1007/s10439-007-9289-7

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  • DOI: https://doi.org/10.1007/s10439-007-9289-7

Keywords

  • Atherosclerosis
  • Carotid artery
  • AR
  • Fuzzy weighting pre-processing
  • LSSVM