Advertisement

Quantifying Carotid Pulse Waveforms Using Subpixel Image Registration

  • Amir HajiRassouliha
  • Emily J. Lam Po Tang
  • Martyn P. Nash
  • Andrew J. Taberner
  • Poul M. F. Nielsen
  • Yusuf O. Cakmak
Conference paper

Abstract

Cardiovascular diseases are a common cause of death. Symptoms of cardiovascular disease often arise at a stage of the disease where treatments are ineffective. Hence, methods that can help early diagnosis of heart problems are essential for preventing heart failure. Assessing the shape of the carotid artery waveforms is one of the methods that clinicians use to diagnose heart and valvular diseases, such as hypertrophic obstructive cardiomyopathy, aortic stenosis, and aortic regurgitation. The carotid artery waveforms may be estimated using pulsed-Doppler ultrasound devices or quantified using catheterisation. However, both of these solutions have limitations. Currently, among available solutions, there is no inexpensive, non-invasive objective method, or diagnostic tool for estimating or quantifying the carotid waveforms. To address these limitations, we have designed a portable non-contact camera-based device to quantify the carotid arterial waveforms. The proposed device calculates the vessel-induced deformation of skin from videos taken from the neck to estimate the carotid artery pressure waveforms. This device takes advantage of our precise and sensitive subpixel image registration algorithm to measure skin deformations from sequential frames of the videos. The skin deformations obtained using our device were compared against a laser displacement measurement device with a resolution of 0.2 μm, and a correlation score of 0.95 was achieved for five subjects.

The carotid artery waveforms measured using this device can provide beneficial information for early detection of heart disease. Furthermore, the data gathered using this device can be used to develop computational models of the carotid artery and/or the cardiac systolic and diastolic phases.

Keywords

Carotid artery Pressure Deformation Subpixel image registration 

References

  1. 1.
    Townsend N, Wilson L, Bhatnagar P, Wickramasinghe K, Rayner M, Nichols M (2016) Cardiovascular disease in Europe: epidemiological update 2016. Eur Heart J 37:3232–3245CrossRefGoogle Scholar
  2. 2.
    Bickley LS, Szilagyi PG, Hoffman RM (2017) Bates’ guide to physical examination and history-taking. Wolters Kluwer, PhiladelphiaGoogle Scholar
  3. 3.
    Chutka DS (2001) A practical guide to clinical medicine. Mayo Clin Proc 76:962CrossRefGoogle Scholar
  4. 4.
    Soleimani E, Mokhtari-Dizaji M, Nasser Fatouraee A, Saberi H (2017) Assessing the blood pressure waveform of the carotid artery using an ultrasound image processing method. Ultrasonography 36(2):144CrossRefGoogle Scholar
  5. 5.
    Veselka J, Anavekar NS, Charron P (2017) Hypertrophic obstructive cardiomyopathy. Lancet 389:1253–1267CrossRefGoogle Scholar
  6. 6.
    Kasper D, Fauci A, Hauser S, Longo D, Jameson JL, Loscalzo J (2015) Harrison’s principles of internal medicine. McGraw Hill, New YorkGoogle Scholar
  7. 7.
    Maron BJ (2002) Hypertrophic cardiomyopathy: a systematic review. JAMA 287:1308–1320Google Scholar
  8. 8.
    Cheng H-M, Chuang S-Y, Wang J-J, Shih Y-T, Wang H-N, Huang C-J, Huang J-T, Sung S-H, Lakatta EG, Yin FCP, Chou P, Yeh C-J, Bai C-H, Pan W-H, Chen C-H (2016) Prognostic significance of mechanical biomarkers derived from pulse wave analysis for predicting long-term cardiovascular mortality in two population-based cohorts. Int J Cardiol 215:388–395CrossRefGoogle Scholar
  9. 9.
    Bonnet B, Jourdan F, du Cailar G, Fesler P (2017) Non invasive evaluation of left ventricular elastance according to pressure-volume curves modeling in arterial hypertension. Am J Physiol Heart Circ Physiol 313:H237CrossRefGoogle Scholar
  10. 10.
    O’Rourke MF, Pauca A, Jiang X-J (2001) Pulse wave analysis. Br J Clin Pharmacol 51: 507–522CrossRefGoogle Scholar
  11. 11.
    Casaccia S, Sirevaag EJ, Richter EJ, O’Sullivan JA, Scalise L, Rohrbaugh JW (2016) Features of the non-contact carotid pressure waveform: cardiac and vascular dynamics during rebreathing. Rev Sci Instrum 87:102501CrossRefGoogle Scholar
  12. 12.
    Almeida VG, Pereira HC, Pereira T, Figueiras E, Borges E, Cardoso JMR, Correia C (2011) Piezoelectric probe for pressure waveform estimation in flexible tubes and its application to the cardiovascular system. Sensors Actuators A Phys 169:217–226CrossRefGoogle Scholar
  13. 13.
    Amelard R, Hughson RL, Greaves DK, Pfisterer KJ, Leung J, Clausi DA, Wong A (2017) Non-contact hemodynamic imaging reveals the jugular venous pulse waveform. Sci Rep 7:1–10CrossRefGoogle Scholar
  14. 14.
    Swinehart DF (1962) The Beer-Lambert law. J Chem Educ 39:333CrossRefGoogle Scholar
  15. 15.
    Tarvainen MP, Ranta-aho PO, Karjalainen PA (2002) An advanced detrending method with application to HRV analysis. IEEE Trans Biomed Eng 49:172–175CrossRefGoogle Scholar
  16. 16.
    HajiRassouliha A, Taberner AJ, Nash MP, Nielsen PM (2017) Subpixel phase-based image registration using Savitzky–Golay differentiators in gradient-correlation. Comput Vis Image Underst.  https://doi.org/10.1016/j.cviu.2017.11.003
  17. 17.
    HajiRassouliha A, Taberner AJ, Nash MP, Nielsen PMF (2016) Subpixel measurement of living skin deformation using intrinsic features. In: Proceedings of computational biomechanics of medicine XI workshop, MICCAIGoogle Scholar
  18. 18.
    HajiRassouliha A, Taberner AJ, Nash MP, Nielsen PMF (2017) Motion correction using subpixel image registration. In: Proceedings of international workshop on reconstruction and analysis of moving body organs, MICCAI, pp 14–23CrossRefGoogle Scholar
  19. 19.
    Ahlgren P, Jarneving B, Rousseau R (2003) Requirements for a cocitation similarity measure, with special reference to Pearson’s correlation coefficient. J Am Soc Inf Sci Technol 54: 550–560CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Amir HajiRassouliha
    • 1
  • Emily J. Lam Po Tang
    • 1
  • Martyn P. Nash
    • 1
    • 2
  • Andrew J. Taberner
    • 1
    • 2
  • Poul M. F. Nielsen
    • 1
    • 2
  • Yusuf O. Cakmak
    • 3
  1. 1.Auckland Bioengineering Institute (ABI), The University of AucklandAucklandNew Zealand
  2. 2.Department of Engineering ScienceThe University of AucklandAucklandNew Zealand
  3. 3.Department of AnatomyUniversity of OtagoDunedinNew Zealand

Personalised recommendations