Acoustic Characterization of Prosthetic Heart Valves

  • G. H. Thomas
  • J. V. Candy
  • D. Perkins
  • R. D. Huber
  • M. Axelrod
Part of the Review of Progress in Quantitative Nondestructive Evaluation book series (RPQN, volume 18 A)


Prosthetic heart valves are a blessing for people with defective heart valves. One type of mechanical heart valve that was manufactured between 1979 and 1985 has been implanted in approximately 86,000 people. Between 500 and 600 of these valves are known to have failed. The failure occurs when a thin wire strut breaks free, see Figure 1. The strut has two legs that are attached to the main body of the heart valve. Typically one of the legs breaks first, leaving the other leg intact and the heart valve still functioning. This condition is called a single leg separation. A technique that analyzes the sound generated by the heart valve was developed to detect this single leg separation. Acoustic data was acquired from implanted heart valves prior to their being explanted. These signals were processed and distinguishing characteristics have been identified that correlated the condition of the heart valve strut with its acoustic signature. An automated classification algorithm was developed and trained to predict the heart valve’s condition.


Heart Valve Probabilistic Neural Network Prosthetic Heart Valve Mechanical Heart Valve Acoustic Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Candy, J. and Barnes, F., Heart valve processing: a feasibility study. Lawrence Livermore National Laboratory Report. (1991) UCRL-ID-107630.Google Scholar
  2. 2.
    del Grande, N.K., Clark, G.A., Durbin, P.F., Fields, DJ., Hernandez, J.E., and Sherwood, R.J. Buried Object Remote Detection Technology for Law Enforcement. SPIE Orlando ‘91 Symposium, Orlando, Florida, April 1–5, 1991.Google Scholar
  3. 3.
    Ballard, Dana H. and Brown, Christopher M. Computer Vision. Englewood, NJ: Prentice-Hall; 1982.Google Scholar
  4. 4.
    Jain, A.K. Fundamentals of Digital Image Processing. Englewood, NJ: Prentice-Hall; 1989.MATHGoogle Scholar
  5. 5.
    Weschler, H. Computational Vision. San Diego, CA: Academic Press; 1990.Google Scholar
  6. 6.
    Duda, R.O. and Hart, P.E. Pattern Classification and Scene Analysis. New York, NY: Wiley; 1973.MATHGoogle Scholar
  7. 7.
    Kohonen, T. Self-Organization and Associative Memory. New York, NY: Springer-Verlag; 1989.CrossRefGoogle Scholar
  8. 8.
    Mar, D. Vision. New York, NY. W. H. Freeman and Co; 1982.Google Scholar
  9. 9.
    Pratt, W.K. Digital Image Processing. New York, NY: Wiley; 1978.Google Scholar
  10. 10.
    Rosenfeld, A. Computer Vision: Basic Principles. Proceedings of the IEEE. 1988; 76: 863–8.Google Scholar
  11. 11.
    Rumelhart, D. E. and McClelland, J.L., “Parallel Distributed Processing: Explorations in the Microstructure of Cognition” Vol. 1: Foundations, MIT Press, 1986.Google Scholar
  12. 12.
    Specht, D. F., “Probabilistic Neural Networks,” Vol. 3, Neural Networks, 1990.Google Scholar

Copyright information

© Springer Science+Business Media New York 1999

Authors and Affiliations

  • G. H. Thomas
    • 1
  • J. V. Candy
    • 1
  • D. Perkins
    • 1
  • R. D. Huber
    • 1
  • M. Axelrod
    • 1
  1. 1.Lawrence Livermore National LaboratoryLivermoreUSA

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