Noninvasive Assistive Method to Diagnose Arterial Disease-Takayasu’s Arteritis

  • Suganthi LakshmananEmail author
  • Dipanjan ChatterjeeEmail author
  • Manivannan MuniyandiEmail author
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 28)


Takayasu’s arteritis (TA) is a rarely studied primary systemic vasculitis involving the aorta and other major arteries of the body. This work proposes a novel noninvasive assessment method, combining both time domain and frequency domain analysis of peripheral signals such as photo plethysmography (PPG) in normal and TA patients providing information about the severity of the TA disease. The novelty of the proposed method is twofold: one, a novel signal processing technique Auto-correlated Spectrum for analyzing PPG signals, and two, use of noninvasive techniques from multiple-site PPG for quantifying the severity. PPG from twenty TA patients and twenty normal subjects have been acquired from five different peripheral sites in the body and compared. The Auto-correlated Spectrums of multiple-site PPG signals are calculated. A novel parameter called P-measure is derived using the relation between the number of peaks and the average distance of the peaks from origin of the spectrum. P-measure is used for classifying normal and diseased using a binary classification method, when greater than or equal to 0.32 the subject is considered as normal and otherwise diseased. The sensitivity and specificity values of this classification method are 96 and 83% respectively. This method is also compared with other frequency domain analysis and this technique can be a simple cost-effective assessment tool to reduce cardiovascular morbidity and mortality in the rarely studied TA, and perhaps other arterial diseases. The small group of TA population due to the rarity of TA disease is a major problem in acquiring data.


Power spectrum density (PSD) Fast fourier transform (FFT) Autocorrelation Takayasu’s arteritis (TA) Photoplethysmography (PPG) 



We thank Dr. George Joseph of Department of Cardiology, Dr. Debashish Danda of Department of Clinical Immunology and Rheumatology, Nisan kunju and Dr. Suresh Devasahayam of Department of Bioengineering, Christian Medical College, Vellore, India. No conflict of interest has been declared by the authors.


  1. 1.
    Subramanyan, R., Joy, J., Balakrishnan, K.G.: Natural history of aortoarteritis (Takayasu9s disease). Circulation 80(3), 429–437 (1989)CrossRefGoogle Scholar
  2. 2.
    Ishikawa, K., Maetani, S.: Long-term outcome for 120 Japanese patients with Takayasu9s disease. Clinical and statistical analyses of related prognostic factors. Circulation 90(4), 1855–1860 (1994)CrossRefGoogle Scholar
  3. 3.
    Schmidt, W.A., Nerenheim, A., Seipelt, E., Poehls, C., Gromnica-Ihle, E.: Diagnosis of early Takayasu arteritis with sonography. Rheumatology 41(5), 496–502 (2002)CrossRefGoogle Scholar
  4. 4.
    Rein, O.R., Markku, M.K.: Carotid and femoral artery stiffness in Takayasu’s arteritis. Scand. J. Rheumatol. 31(2), 85–88 (2002)CrossRefGoogle Scholar
  5. 5.
    Hertzman, A.B.: Photoelectric plethysmography of the fingers and toes in man. Proc. Soc. Exp. Biol. Med. 37(3), 529–534 (1937)CrossRefGoogle Scholar
  6. 6.
    Allen, J.: Photoplethysmography and its application in clinical physiological measurement. Physiol. Meas. 28(3), R1 (2007)CrossRefGoogle Scholar
  7. 7.
    Eldrup-Jorgensen, S.V., Schwartz, S.I., Wallace, J.D.: A method for clinical evaluation of peripheral circulation: photoelectric hemodensitometry. Surgery 59(4), 505–513 (1966)Google Scholar
  8. 8.
    Simonson, E.: Photoelectric plethysmography; methods, normal standards, and clinical application. Geriatrics 11(10), 425 (1956)Google Scholar
  9. 9.
    Zheng, D., Allen, J., Murray, A.: Development of a method for determining arterial pulse propagation times and influence of arterial compliance. In: IEEE Computers in Cardiology, pp. 289–292 (2006)Google Scholar
  10. 10.
    Malvezzi, L., Castronuovo, J.J., Swayne, L.C., Cone, D., Trivino, J.Z.: The correlation between three methods of skin perfusion pressure measurement: radionuclide washout, laser doppler flow, and photoplethysmography. J. Vasc. Surg. 15(5), 823–830 (1992)CrossRefGoogle Scholar
  11. 11.
    Nitzan, M., Babchenko, A., Milston, A., Turivnenko, S., Khanokh, B., Mahler, Y.: Measurement of the variability of the skin blood volume using dynamic spectroscopy. Appl. Surf. Sci. 106, 478–482 (1996)CrossRefGoogle Scholar
  12. 12.
    Nitzan, M., Babchenko, A., Shemesh, D., Alberton, J.: Influence of thoracic sympathectomy on cardiac induced oscillations in tissue blood volume. Med. Biol. Eng. Comput. 39(5), 579–583 (2001)CrossRefGoogle Scholar
  13. 13.
    Nitzan, M., de Boer, H., Turivnenko, S., Babchenko, A., Sapoznikov, D.: Power spectrum analysis of spontaneous fluctuations in the photoplethysmographic signal. J. Basic Clin. Physiol. Pharmacol. 5(3–4), 269–276 (1994)Google Scholar
  14. 14.
    Nitzan, M., Turivnenko, S., Milston, A., Babchenko, A., Mahler, Y.: Low-frequency variability in the blood volume and in the blood volume pulse measured by photoplethysmography. J. Biomed. Opt. 1(2), 223–229 (1996)CrossRefGoogle Scholar
  15. 15.
    Oliva, I., Ipser, J., Roztocĭl, K., Guttenbergerova, K.: Fourier analysis of the pulse wave in obliterating arteriosclerosis. VASA. Zeitschrift für Gefässkrankheiten 5(2), 95 (1976)Google Scholar
  16. 16.
    Sherebrin, M.H., Sherebrin, R.Z.: Frequency analysis of the peripheral pulse wave detected in the finger with a photoplethysmograph. IEEE Trans. Biomed. Eng. 37(3), 313–317 (1990)CrossRefGoogle Scholar
  17. 17.
    Ingle, V.K., Proakis, J.G.: A Self-Study Guide for Digital Signal Processing, 3rd ed. Prentice Hall (2003)Google Scholar
  18. 18.
    Nitzan, M., Vatine, J.J., Babchenko, A., Khanokh, B., Tsenter, J., Stessman, J.: Simultaneous measurement of the photoplethysmographic signal variability in the right and left hands. Lasers Med. Sci. 13(3), 189–195 (1998)CrossRefGoogle Scholar
  19. 19.
    Proakis, J.G., Manolakis, D.G.: Digital Signal Processing (1996)Google Scholar
  20. 20.
    Allen, J.: Measurement and analysis of multi-site photoplethysmographic pulse waveforms in health and arterial disease. Doctoral Dissertation, University of Newcastle upon Tyne (2002)Google Scholar
  21. 21.
    Allen, J., Murray, A.: Similarity in bilateral photoplethysmographic peripheral pulse wave characteristics at the ears, thumbs and toes. Physiol. Meas. 21(3), 369 (2000)CrossRefGoogle Scholar
  22. 22.
    Allen, J., Murray, A.: Variability of photoplethysmography peripheral pulse measurements at the ears, thumbs and toes. IEE Proc. Sci. Meas. Technol. 147(6), 403–407 (2000)CrossRefGoogle Scholar
  23. 23.
    Erts, R., Spigulis, J., Kukulis, I., Ozols, M.: Bilateral photoplethysmography studies of the leg arterial stenosis. Physiol. Meas. 26(5), 865 (2005)CrossRefGoogle Scholar
  24. 24.
    Spigulis, J.: Optical noninvasive monitoring of skin blood pulsations. Appl. Opt. 44(10), 1850–1857 (2005)CrossRefGoogle Scholar
  25. 25.
    Hayes, M.H.: Statistical Digital Signal Processing and Modeling. Wiley, New York (1996)Google Scholar
  26. 26.
    Stoica, P., Moses, R.L.: Introduction to Spectral Analysis, vol. 1, pp. 3–4. Prentice hall, Upper Saddle River (1997)Google Scholar
  27. 27.
    Welch, P.: The use of fast Fourier transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms. IEEE Trans. Audio Electroacoust. 15(2), 70–73 (1967)CrossRefGoogle Scholar
  28. 28.
    Salahuddin, L., Jeong, M.G., Kim, D.: Ultra short term analysis of heart rate variability using normal sinus rhythm and atrial fibrillation ECG data. In: IEEE 9th International Conference on e-Health Networking, Application and Services, pp. 240–243 (2007)Google Scholar
  29. 29.
    Brennan, M., Palaniswami, M., Kamen, P.: Do existing measures of poincare plot geometry reflect nonlinear features of heart rate variability? IEEE Trans. Biomed. Eng. 48(11), 1342–1347 (2001)CrossRefGoogle Scholar
  30. 30.
    Fletcher, R.H., Fletcher, S.W.: Clinical epidemiology: the essentials, 4th edn, pp. 156–199. Lippincott Williams & Wilkins, Baltimore (2005)Google Scholar
  31. 31.
    Anne, M.R.A., Arthur, F.D.: Grant’s Atlas of Anatomy, 12th ed., pp 355–607. Lippincott Williams and Wilkins (2009)Google Scholar
  32. 32.
    Rowell, L.B.: Human cardiovascular Control. Oxford University Press, New York (1993)Google Scholar

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© Springer International Publishing AG  2018

Authors and Affiliations

  1. 1.Department of Biomedical EngineeringSSN College of EngineeringChennaiIndia
  2. 2.Department of Applied MechanicsIndian Institute of Technology MadrasChennaiIndia

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