Publications
International Journal on Smart Sensing and Intelligent Systems
Professor Subhas Chandra Mukhopadhyay
Exeley Inc. (New York)
Subject: Computational Science & Engineering, Engineering, Electrical & Electronic
eISSN: 1178-5608
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ULTRASONIC BLOOD FLOW SENSING USING DOPPLER VELOCIMETRY
Michelle Case / Matthew Micheli / Daniel Arroyo / Jeremy Hillard / Martin Kocanda
Keywords : cardiac sensing, digital filters, Doppler Effect, Matlab™, signal processing, spectrogram, ultrasonic flow meter, velocimetry, velocity profiles.
Citation Information : International Journal on Smart Sensing and Intelligent Systems. Volume 6, Issue 4, Pages 1,298-1,316, DOI: https://doi.org/10.21307/ijssis-2017-591
License : (CC BY-NC-ND 4.0)
Received Date : 23-June-2013 / Accepted: 10-August-2013 / Published Online: 03-September-2013
- ARTICLE
- FIGURES & TABLES
- REFERENCES
- EXTRA FILES
- COMMENTS
ARTICLE
ABSTRACT
Ultrasonic blood flow sensing is a non-invasive method for measuring blood flow velocity. The objective of this work is to produce a low-cost ultrasonic blood flow instrument utilizing Doppler shifted signals and enhanced signal processing methods. The instrument transmits a single-frequency signal into circulatory tissues and receives a signal that is Doppler shifted in proportion to velocity. Subsequent processing techniques produce an audio feedback signal whereby the user “hears” the flow characteristics. The circuit used consists of a transmitter, receiver, and frequency shifter. Signal processing is performed externally to produce velocity profiles of arterial blood flow using Matlab™ to create spectrograms and low-pass filtering on the recorded feedback signals. Spectrogram provide mapping of the velocity profiles. Resultant mapping indicate that velocity profiles have a roughly parabolic shape and that filtering is required to reduce high frequency noise. Signals were obtained using multiple cardiac stressors to determine the flow sensing performance.
FIGURES & TABLES
REFERENCES
[1] Y. A. Cengal and J. M. Cimbala, “Fluid Kinematics and Internal Flow,” Fluid Mechanics Fundamentals and Applications, 2nd ed. New York, NY: McGraw Hill, 2010, pp. 131-389.
[2] D. Shetty and R. A. Kolk, “Sensors and Transducers,” Mechatronics System Design, 2nd ed. Stamford, CT: Cengage Learning, 2011, pp. 203-234.
[3] J. Webster, “Measurement of Flow and Volume of Blood,” Medical Instrumentation Application and Design, 4th ed. Danvers, MA: John Wiley & Sons, Inc., 2009, pp. 356-379.
[4] J. Solano et al. “Doppler Ultrasound Blood Flow Measurement System for Assessing Coronary Revascularization”, WORLDCOMP, July 2011. [5] Brown, H. S., et al. “Measurement of normal portal venous blood flow by Doppler ultrasound”, Gut 30.4, 1989, pp. 503-509. [6] Gabe, Ivor T., et al. “Measurement of instantaneous blood flow velocity and pressure in conscious man with a catheter-tip velocity probe”, Circulation 40.5, 1969, pp. 603-614.
[7] Kuo, Sen M. et al. “Power Spectrum Density”, Real-Time Digital Signal Processing: Implementations and Applications, 2nd ed. Hoboken, NJ: John Wiley & Sons Inc., 2007, pp. 325-329.
[8] Hoang N. C., Hayes-Gill, B. R., Zhu, Y., Crowe, J. A., He, D., Morgan, S.P. “Low resource processing algorithms for laser Doppler blood flow imaging”. Medical Engineering & Physics, 33(6), July 2011, pp. 720-729.
[9] Gao, L., Zhang, Y., Zhang, K., Cai, G., Zhang, J. Shi, X., “A computer simulation model for Doppler ultrasound signals from pulsatile blood flow in stenosed vessels”, Computers in Biology and Medicine, 42(9), 2012, pp. 906-91.