Quantifying Carotid Pulse Waveforms Using Subpixel Image Registration

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


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.


Carotid artery Pressure Deformation Subpixel image registration 


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Amir HajiRassouliha
    • 1
    Email author
  • 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

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