Advertisement

Application of the matching pursuit method for structural decomposition and averaging of phonocardiographic signals

  • H. Sava
  • P. Pibarot
  • L. -G. Durand
Article

Abstract

The paper evaluates the performance of an automatic adaptive time-frequency method to detect each cardiac cycle of a phonocardiogram (PCG) and extract average heart sounds and PCG cycles. The proposed method combines a global search of the PCG, in terms of the energy distribution of the most important components, with a local search relating to the specific events found within a cardiac cycle. The method is applied to 100 PCG recordings from 50 patients with an aortic bioprosthetic valve. The performance of the proposed method is compared with a commonly used semi-automatic method that is based on the combined analysis of an electrocardiogram (ECG) and the PCG signal. Results show that the proposed method clearly outperforms the semiautomatic method, especially in the case of patients with malfunctioning bioprostheses. By eliminating the need to record an ECG as the time-reference signal, this method reduces hardware overheads when analysis of PCG signals is the primary aim. It is also independent of subjective human judgment for selection of reference templates and threshold levels. Furthermore, the method is robust to artefacts, background noise and other kinds of signal interferences. With minor modifications, the procedure described could be applied to other types of biomedical signal in order to extract coherent transient components and identify specific events.

Keywords

Phonocardiography Adaptive time-frequency analysis Heart sound averaging 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Baranek, H. L., Lee, H. C., Gloutier, D., andDurrand, L.-G. (1989): ‘Automatic detection of sounds and murmurs in patients with Ionescu-Shiley aortic bioprostheses,’Med. Biol. Eng. Comput.,27, pp. 449–455CrossRefGoogle Scholar
  2. Chen, D., Durrand, L.-G., andLee, H. C. (1977a): ‘Time-frequency analysis of the first heart sound. Part 1: Simulation and analysis’,Med. Biol. Eng. Comput.,35, pp. 306–310Google Scholar
  3. Chen, D., Durand, L.-G., Guo, Z., andLee, H. C. (1997b): ‘Time-frequency analysis of the first heart sound. Part 2: An appropriate time-frequency representation technique’,Med. Biol. Eng. Comput.,35, pp. 311–317CrossRefGoogle Scholar
  4. Chen, D., Durand, L.-G., Lee, H.C., andWieting, D. W. (1997c): ‘Time-frequency analysis of the first heart sound. Part 3: Application to dogs with varying cardiac contractility and to patients with mitral mechanical prosthetic heart valves’,Med. Biol. Eng. Comput.,35, pp. 455–461CrossRefGoogle Scholar
  5. Debias, F., Durand, L.-G., Pibarot, P., andGuardo, R. (1977a): ‘Time-frequency analysis of heart murmurs. Part I: Parametric modelling and numerical simulations’,Med. Biol. Eng. Comput.,35, pp. 474–479Google Scholar
  6. Debias, F., Durand, L.-G., Guo, Z., andGuardo, R. (1997b): ‘Time-frequency analysis of heart murmurs. Part II: Optimisation of time-frequency representations and performance evaluation,’Med. Biol. Eng. Comput.,35, pp. 480–485Google Scholar
  7. Durand, L.-G., andPibarot, Ph. (1995): ‘Digital signal processing of the phonocardiogram: review of the most recent advancements,’Crit. Rev. Biomed. Eng.,23 (3/4), pp. 163–219Google Scholar
  8. Durand, L.-G. (1994): ‘Evaluation of prosthetic heart valve function by signal processing of heart valve sounds’Med. & Life Sci. Eng. J. Biomed. Eng. Soc. India,13, pp. 39–59Google Scholar
  9. Freisen, G. M., Jannett, T. C., Jadallah, M. A., Yates, S. L., Quint, S. R., andNagle, H. T. (1990): ‘A comparison of the noise sensitivity of nine QRS detection algorithms’,IEEE Trans.,BME-37, (1), pp. 85–98Google Scholar
  10. Hlawatsch, F., andBoudreaux-Bartels, G. F. (1992): ‘Linear and quadratic time-frequency signal representations’,IEEE Signal Process Mag., April, pp. 21–67CrossRefGoogle Scholar
  11. Lehner, R., andRangayyan, R. (1988): ‘A three-channel micro-computer system for segmentation and characterisation of the phonocardiogram’,IEEE Trans. On BME,34, (6), pp. 485–489.Google Scholar
  12. Li, C., Zheng, Ch., andTai, Ch. (1995): ‘Detection of ECG characteristic points using wavelet transforms’IEEE Trans.,BME-42, (1), pp. 21–28MATHGoogle Scholar
  13. Mallat, S. G., andZhang, Zh. (1993): ‘Matching pursuits with time-frequency dictionaries’,IEEE Trans. Signal Process.,41, (12), pp. 3397–3419MATHCrossRefGoogle Scholar
  14. Rangayyan, R. M., andLehner, R. J. (1988): ‘Phonocardiogram signal anlaysis: a review’,Crit. Rev. Biomed. Eng.,15, (3), pp. 211–236Google Scholar
  15. Sava, H., andMcDonnell, J. T. (1996a): ‘Frequency characteristics of sounds produced by mechanical prosthetic heart valves’,Innov. Technol. Biol. Med. 17, (1), pp. 71–88Google Scholar
  16. Sava, H., andMcDonnell, J. T. (1996b): ‘Spectral composition of heart sounds before and after mechanical heart valve implantation using a modified forward-backward Prony’s method’,IEEE Trans. BME-43, (7), pp. 734–742Google Scholar
  17. Sava, H., andMcDonnell, J. T. (1996c): ‘Spectral characterisation and classification of Carpentier-Edwards heart valves impanted in the aortic position’,IEEE Trans.,BME-43, (10), pp. 1046–1048Google Scholar
  18. Taswell, C. (1996): ‘Satisfy search algorithms for selecting nearbest bases in adaptive tree-structured wavelet transforms’,IEEE Trans. Signal Process. 44, (10), pp. 2423–2438CrossRefGoogle Scholar
  19. Unser M., andAldroubi, A. (1996): ‘A review of wavelets in biomedical applications’,Proc. IEEE,84, (2), pp. 626–638CrossRefGoogle Scholar
  20. Wood, J. C., andBarry, T. D. (1995): ‘Time-frequency analysis of the first heart sounds’,IEEE Eng. Med. Biol. Mag.,14, (2), pp. 144–151CrossRefGoogle Scholar
  21. Zhang, X., Durand, L.-G., Senhadjii, L., Hee, H. C., Coatrieux, J.-L. (1996): ‘Application of the matching pursuit method for the analysis and synthesis of the phonocardiogram.’ Proc. IEEE-EMBS 96, Paper 448Google Scholar

Copyright information

© IFMBE 1998

Authors and Affiliations

  1. 1.Laboratory of Biomedical Engineering, IRCMUniversité de MontrealMontrealCanada

Personalised recommendations