Advertisement

Heart Sounds Classification with a Fuzzy Neural Network Method with Structure Learning

  • Lijuan Jia
  • Dandan Song
  • Linmi Tao
  • Yao Lu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7368)

Abstract

Heart sound analysis is a basic method for cardiac evaluation, which contains physiological and pathological information of various parts of the heart and interactions between them. This paper aims to design a system for analyzing heart sounds including automatic analysis and classification. With the features extracted by wavelet decomposition and Normalized Average Shannon Energy, a novel fuzzy neural network method with structure learning is proposed for the heart sound classification. Experiments with real data demonstrated that our approach can correctly classify all the tested heart sounds even for the ones with previous unseen heart diseases.

Keywords

Heart sounds Fuzzy neural network Structure learning 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Groch, M.W., Domnanovich, J.R., Erwin, W.D.: A new heart-sounds gating device for medical imaging Biomedical Engineering. IEEE Transactions on Biomedical Eng. 39(3), 307–310 (1992)CrossRefGoogle Scholar
  2. 2.
    Lehner, R.J., Rangayyan, R.M.: A three-channel microcomputer system for segmentation and characterization of the phonocardiogram. IEEE Trans. Biomedical Eng. 34(6), 485–489 (1987)CrossRefGoogle Scholar
  3. 3.
    Quan, H., Wang, W.: Extraction of the First and the Second Heart Sounds Based on Multi-reisolution Analysis of Wavelet Transform. Beijing Biomedical Engineering, 64–66 (2004)Google Scholar
  4. 4.
    Haghighi-Mood, A., Torry, J.N.: A Sub-Band Energy Tracking Algorithm for Heart Sound Segmentation. Computers in Cardiology, 501–504 (1995)Google Scholar
  5. 5.
    Xu, X., Lin, Y., Yan, B.: Envelope Extraction of Heart Sound based on Hilbert-Huang Transform 21(2), 134–136 (2008)Google Scholar
  6. 6.
    Zhou, J., Yang, Y., He, W.: Heart sounds signal analysis and its featues extraction method research. China’s Biological Medical Engineering (6), 685–689 (2005)Google Scholar
  7. 7.
    Schmidt, S.E., Toft, E., Holst-Hansen, C., Graff, C., Struijk, J.J.: Segmentation of Heart Sound Recordings from an Electronic Stethoscope by a Duration Dependent Hidden-Markov Model. Computers in Cardiolog, 345–348 (2008)Google Scholar
  8. 8.
    Chen, M., Ye, D., Chen, J.: Segmentation of Heart Sound based on The Signal Envelope and Short-term Zero Rate. Beijing Biomedical Engineering 26(1), 48–51 (2007)Google Scholar
  9. 9.
    Liang, H., Lukkarinen, S., Hartimo, I.: Heart Sound Segrnentation Algorithm Based on Heart Sound Envelogram. Computers in Cardiolog., 105–108 (1997)Google Scholar
  10. 10.
    Vapnik, V.: Statistical Learning Theory, vol. 1, pp. 9–13. Wiley, New York (1998)zbMATHGoogle Scholar
  11. 11.
    Zadeh, L.A.: Fuzzy Sets. Information and Control 8(3), 338–353 (1965)MathSciNetzbMATHCrossRefGoogle Scholar
  12. 12.
    Song, A., Deng, Z.: A Novel ncRNA Gene Prediction Approach Based on Fuzzy Neural Networks with Structure LearningGoogle Scholar
  13. 13.
    Liang, H., Sakari, L., Iiro, H.: A Heart Sound Segmentation Algorithm Using Wavelet Decomposition and Reconstruction. In: 19th International Conference IEEE/EMBS, pp. 1630–1633. IEEE, Chicago (1997)Google Scholar
  14. 14.
    Xie, M., Guo, X., Yang, Y.: A Study of Quantification Method for Heart Murmur Grading. A Murmur Energy Ratio Method. Bioinformatics and Biomedical Engineering (2010)Google Scholar
  15. 15.
    Sun, Z., Deng, Z.: A fuzzy neural network and its application to controls. Artificial Intelligence in Engineering 10, 311–315 (1996)CrossRefGoogle Scholar
  16. 16.
    Albus, J.: A new approach to manipulator control: the cerebella model articulation controller (CMAC). E. J. Dyn. Sys. Meas. Trans. ASM 97, 220–227 (1975)zbMATHCrossRefGoogle Scholar
  17. 17.
    Takagi, T., Sugeno, M.: Derivation of fuzzy control rules from human operator’s control actions. In: The IFAC Symposium on Fuzzy Information, Knowledge Representation and Decision Analysis, pp. 55–60 (1983)Google Scholar
  18. 18.
    Kosko, B.: Neural Networks and Fuzzy Systems: A Comprehensive Foundation to Machine Intelligence. Prentice-Hall, Upper Saddle River (1992)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Lijuan Jia
    • 1
  • Dandan Song
    • 1
  • Linmi Tao
    • 2
  • Yao Lu
    • 1
  1. 1.Lab of High Volume Language Information Processing & Cloud Computing, Beijing Lab of Intelligent Information Technology School of Computer ScienceBeijing Institute of TechnologyBeijingChina
  2. 2.Department of Computer Science and TechnologyTsinghua UniversityBeijingChina

Personalised recommendations