Table of Contents Author Guidelines Submit a Manuscript
Journal of Electrical and Computer Engineering
Volume 2015, Article ID 274541, 10 pages
http://dx.doi.org/10.1155/2015/274541
Research Article

Time-Frequency Analysis of Clinical Percussion Signals Using Matrix Pencil Method

1Department of Physics, University of Windsor, 401 Sunset Avenue, Windsor, ON, Canada N9B 3P4
2Tessonics Corp., 2019 Hazel Street, Birmingham, MI 48009, USA
3Institute for Diagnostic Imaging Research, University of Windsor, 401 Sunset Avenue, Windsor, ON, Canada N9B 3P4
4Detroit Medical Center, 4201 St. Antoine Street, Detroit, MI 48201, USA

Received 30 September 2014; Accepted 1 March 2015

Academic Editor: Peter Jung

Copyright © 2015 Moinuddin Bhuiyan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. J. C. Yernault and A. B. Bohadana, “Chest percussion,” European Respiratory Journal, vol. 8, no. 10, pp. 1756–1760, 1995. View at Publisher · View at Google Scholar · View at Scopus
  2. A. Murray and J. M. M. Neilson, “Diagnostic percussion sounds: 1. A qualitative analysis,” Medical and Biological Engineering, vol. 13, no. 1, pp. 19–28, 1975. View at Publisher · View at Google Scholar · View at Scopus
  3. A. Kacha, F. Grenez, and K. Benmahammed, “Time-frequency analysis and instantaneous frequency estimation using two-sided linear prediction,” Signal Processing, vol. 85, no. 3, pp. 491–503, 2005. View at Publisher · View at Google Scholar · View at Scopus
  4. M. A. Pantea, E. V. Malyarenko, A. E. Baylor, and R. G. Maev, “A physical approach to the automated classification of clinical percussion sounds,” The Journal of the Acoustical Society of America, vol. 131, no. 1, pp. 608–619, 2012. View at Google Scholar
  5. F. Hlawatsch and G. F. Boudreaux-Bartels, “Linear and quadratic time-frequency signal representations,” IEEE Signal Processing Magazine, vol. 9, no. 2, pp. 21–67, 1992. View at Publisher · View at Google Scholar · View at Scopus
  6. H.-I. Choi and W. J. Williams, “Improved time-frequency representation of multicomponent signals using exponential kernels,” IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 37, no. 6, pp. 862–871, 1989. View at Publisher · View at Google Scholar · View at Scopus
  7. G. Putland and B. Boashash, “Can a signal be both monocomponent and multicomponent?” in Proceedings of the 3rd Australasian Workshop on Signal Processing Applications (WoSPA '00), Brisbane, Australia, December 2000.
  8. B. Barkat and B. Boashash, “A high-resolution quadratic time-frequency distribution for multicomponent signals analysis,” IEEE Transactions on Signal Processing, vol. 49, no. 10, pp. 2232–2239, 2001. View at Publisher · View at Google Scholar · View at Scopus
  9. L. A. Escobar-Moreira, “Ultrasonic fault machinery monitoring by using the wigner-ville and Choi-Williams distributions,” in Proceedings of the 11th International Conference on Electrical Machines and Systems (ICEMS '08), pp. 741–745, IEEE, Wuhan, China, October 2008. View at Scopus
  10. Z. Li and M. J. Crocker, “A study of joint time-frequency analysis-based modal analysis,” IEEE Transactions on Instrumentation and Measurement, vol. 55, no. 6, pp. 2335–2342, 2006. View at Publisher · View at Google Scholar · View at Scopus
  11. M. D. Davidović and V. Vojisavljevic, “Time-frequency analysis of nonstationary optical signals using Husimi type function,” Acta Physica Polonica A, vol. 116, no. 4, pp. 675–677, 2009. View at Google Scholar · View at Scopus
  12. C. Griffin, “A comparison study on the Wigner and Choi-Williams distributions for detection,” in Proceedings of the International Conference on Acoustics, Speech, and Signal Processing (ICASSP '91), pp. 1485–1488, May 1991. View at Scopus
  13. Y. Noguchi, K. Watanabe, E.-I. Kashiwagi et al., “Time-frequency analysis with eight-figure kernel,” in Proceedings of the 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 1324–1327, November 1997. View at Scopus
  14. A. Goli, D. M. McNamara, and A. K. Ziarani, “A novel method for decomposition of multicomponent nonstationary signals,” in Proceedings of the IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA '07), pp. 255–258, New Paltz, NY, USA, October 2007. View at Publisher · View at Google Scholar · View at Scopus
  15. T. K. Sarkar and O. Pereira, “Using the matrix pencil method to estimate the parameters of a sum of complex exponentials,” IEEE Antennas and Propagation Magazine, vol. 37, no. 1, pp. 48–55, 1995. View at Publisher · View at Google Scholar · View at Scopus
  16. J. Laroche, “The use of the matrix pencil method for the spectrum analysis of musical signals,” Journal of the Acoustical Society of America, vol. 94, no. 4, pp. 1958–1965, 1993. View at Publisher · View at Google Scholar · View at Scopus
  17. M. Bhuiyan, E. V. Malyarenko, M. A. Pantea, F. M. Seviaryn, and R. Gr. Maev, “Advantages and limitations of using matrix pencil method for the modal analysis of medical percussion signals,” IEEE Transactions on Biomedical Engineering, vol. 60, no. 2, pp. 417–426, 2013. View at Publisher · View at Google Scholar · View at Scopus
  18. M. Bhuiyan, E. V. Malyarenko, M. A. Pantea, R. G. Maev, and A. E. Baylor, “Estimating the parameters of audible clinical percussion signals by fitting exponentially damped harmonics,” The Journal of the Acoustical Society of America, vol. 131, no. 6, pp. 4690–4698, 2012. View at Publisher · View at Google Scholar · View at Scopus
  19. H. Fengduo, T. Sarkar, and H. Yingbo, “The spectral parameter estimation by using pre filtering and matrix pencil method,” in Proceedings of the 5th ASSP Workshop on Spectrum Estimation and Modeling, pp. 45–49, Rochester, NY, USA, October 1990. View at Publisher · View at Google Scholar
  20. Y. Hua and T. K. Sarkar, “Matrix pencil method for estimating parameters of exponentially damped/undamped sinusoids in noise,” IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 38, no. 5, pp. 814–824, 1990. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus