Skip to main content
SpringerLink
Log in

Diagnosing Aortic Valve Stenosis by Parameter Extraction of Heart Sound Signals

  • Published:
Annals of Biomedical Engineering Aims and scope Submit manuscript

Abstract

The objective of this study was to develop an automatic signal analysis system for heart sound diagnosis. This should support the general practitioner in discovering aortic valve stenoses at an early stage to avoid or decrease the number of surgical interventions. The applied analysis method is based on classification of heart sound signals utilising parameter extraction. From the wavelet decomposition of a representative heart cycle as well as from the Short Time Fourier Transform (STFT) and the Wavelet Transform (WT) spectra new time series were derived. In several segments, parameters were extracted and analysed. In addition, features of the Fast Fourier Transform (FFT) of the raw signal were examined. In this study, 206 patients were enrolled, 159 with no heart valve disease or any other heart valve disease but aortic valve stenosis and 47 suffering from aortic valve stenosis in a mild, moderate or severe stage. To separate the groups, a linear discriminant function analysis was applied leading to a reduced parameter set. The introduced two classification stage (CS) system for automatic detection of aortic valve stenoses achieves a high sensitivity of 100% for moderate and severe aortic valve stenosis and a sensitivity of 75% for mild aortic valve stenosis. A specificity of 93.7% for patients without aortic valve stenosis is provided. The developed method is robust, cost effective and easy to use, and could, therefore, be a suitable method to diagnose aortic valve stenosis by general practitioners.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Bulgrin, J. R., B. J. Rubal, C. R. Thompson, and J. M. Moody. Comparison of short-time Fourier, wavelet and time-domain analyses of intercardiac sounds. Biomed. Sci. Instrum. 29:465–472, 1993.

    PubMed  Google Scholar 

  2. Barker, L. F. Electrocardiography and phonocardiography: A collective review. Bull. Johns Hopkins Hosp. 21:358–389, 1910.

    Google Scholar 

  3. Bonferroni, C. E. Il calcolo delle assicurazioni su gruppi di teste. In: Studi in Onore del Professore Salvatore Ortu Carboni. Rome: Italy, 1935, pp. 13–60.

  4. Bonferroni, C. E. Teoria statistica delle classi e calcolo delle probabilità. Pubblicazioni del R Istituto Superiore di Scienze Economiche e Commerciali di Firenze 8:3–62, 1936.

    Google Scholar 

  5. Cathers, I. Neural network assisted cardiac auscultation. Artif. Intell. Med. 7:53–66, 1995.

    Article  PubMed  Google Scholar 

  6. Dimond, E. G., and A. Benchimoi. Phonocardiography in pulmonary stenosis: Special correlation between hemodynamic and phonocardiographic findings. Ann. Intern. Med. 52:145–153, 1960.

    PubMed  Google Scholar 

  7. Durand, L.-G., and P. Pibarot. Digital signal processing of the phonocardiogram: Review of the most recent advancements. Crit. Rev. Biomed. Eng. 23:163–219, 1995.

    PubMed  Google Scholar 

  8. Durand, L.-G., M. Blanchard, G. Cloutier, H. Sabbah, and P. D. Stein. Comparison of pattern recognition methods for computer-assisted classification of spectra of heart sounds in patients with a porcine bioprosthetic valve implanted in the mitral position. IEEE Trans. Biomed. Eng. 37(12):1121–1129, 1990.

    Article  PubMed  Google Scholar 

  9. Durand, L.-G., Z. Guo, H. N. Sabbah, and P. D. Stein. Comparison of spectral techniques for computer assisted classification of spectra of heart sounds in patients with a porcine bioprosthetic valve. Med. Biol. Eng. Comput. 31(3):229–236, 1993.

    PubMed  Google Scholar 

  10. Einthoven, W. The registration of human heart sound using the string galvanometer. Arch. Ges. Physiol. 117:461–478, 1907.

    Article  Google Scholar 

  11. Guo, Z., L.-G. Durand, H. C. Lee, L. Allard, M. C. Grenier, and P. D. Stein. Artificial neural networks in computer-assisted classification of heart sounds in patients with porcine bioprosthetic valves. Med. Biol. Eng. Comput. 32(3):311–316, 1994.

    PubMed  Google Scholar 

  12. Hadjileontiadis, L. J., and S. M. Panas. A wavelet-based reduction of heart sound noise from lung sounds. Int. J. Med. Inf. 52:183–190, 1998.

    Article  Google Scholar 

  13. Harris, F. J. On the use of windows for harmonic analysis with the discrete Fourier transform. Proc. IEEE 66(1):51–83, 1978.

    Google Scholar 

  14. Holldack, K., and K. Gahl. Auscultation and percussion, inspection and palpation (in German: Auskultation und Perkussion, Inspektion und Palpation). Stuttgart, Germany: Georg Thieme Verlag, 1991.

  15. Holldack, K., and H.-W. Rautenberg. Phonocardiography (in German: Phonokardiographie). Stuttgart, Germany: Georg Thieme Verlag, 1979.

  16. Huiying, L., S. Lukkarinen, and I. Hartimo. Heart sound segmentation algorithm based on heart sound envelogram. Comput. Cardiol. 7–10, 1997.

  17. Jamous, G., L.-G. Durand, Y. E. Langlois, T. Lanthier, P. Pibarot, and S. Carioto. Optimal time-window duration for computing time-frequency representations of normal phonocardiograms in dogs. Med. Biol. Eng. Comput. 30(5):503–508, 1992.

    PubMed  Google Scholar 

  18. Kim, D., and M. E. Tavel. Assessment of severity of aortic stenosis through time-frequency analysis of murmur. Chest 124(5):1638–1644, 2003.

    Article  PubMed  Google Scholar 

  19. Lukkarinen, S., K. Sikioe, A.-L. Noponen, A. Angerla, and R. Sepponen (eds.). Novel Software for Real Time Processing of Phonocardiographic Signal. Chicago, USA: Applied Electronics Laboratory, Helsinki University of Technology, 1997.

    Google Scholar 

  20. Maass, H., and A. Weber. Recording of heart sounds using distinguishing filters, a study for standardization of the heart sounds (in German: Herzschallregistrierung mittels differenzierender Filter, eine Studie zur Herzschallnormung). Cardiologica 21:773, 1952.

    Google Scholar 

  21. Mallat, S. G. A theory for multiresolution signal decomposition. IEEE Trans. Pattern Anal. Machine Intell. 11(7):674–693, 1989.

    Article  Google Scholar 

  22. Mardia, K. V., J. T. Kent, and J. M. Bibby. Multivariate Analysis (Probability and Mathematical Statistics). London: Academic Press, 1980.

    Google Scholar 

  23. Messer, S., J. Agzarian, and D. Abbott. Optimal wavelet denoising for phonocardiograms. Microelectron. J. 32:931–941, 2001.

    Article  Google Scholar 

  24. Pavlopoulos, S. A., A. C. Stasis, and E. N. Loukis. A decision tree–based method for the differential diagnosis of Aortic Stenosis from Mitral Regurgitation using heart sounds. Biomed. Eng. Online 3:21, 2004.

    Article  PubMed  Google Scholar 

  25. Rangayyan, R. M., and R. J. Lehner. Phonocardiogram signal analysis: A review. Crit. Rev. Biomed. Eng. 15(3):211–236, 1987.

    PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Medical Engineering, University of Applied Sciences Jena, Carl-Zeiss-Promenade 2, 07745, Jena, Germany

    Andreas Voss (Professor) & Andrea Mix

  2. Medtrans GmbH, Jena, Germany

    Andrea Mix & Thomas Hübner

Authors
  1. Andreas Voss
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Andrea Mix
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Thomas Hübner
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Andreas Voss.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Voss, A., Mix, A. & Hübner, T. Diagnosing Aortic Valve Stenosis by Parameter Extraction of Heart Sound Signals. Ann Biomed Eng 33, 1167–1174 (2005). https://doi.org/10.1007/s10439-005-5347-x

Download citation

  • Received:

  • Accepted:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10439-005-5347-x

Keywords

Search

Navigation