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Second Heart Sound (S2) Decomposition by Hilbert Vibration Decomposition (HVD) for Affective Signal Modeling and Learning

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Advances in Web-Based Learning – ICWL 2013 Workshops (ICWL 2013)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8390))

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Abstract

This article presents a novel signal decomposition method, Hilbert vibration decomposition (HVD), for analyzing one of the major heart sound components second heart sound (S2) signal for affective signal modeling. In this proposed method, three kinds of simulated S2 signals are generated and the typical one is chosen for decomposition. For HVD method, a FIR filter is designed to separate each of the decomposed components. Finally, performance indicators, including the number of decomposed components, Hilbert spectrum, and spectral centroids, are measured.

To evaluate the performance of HVD, the decomposed components are compared with those generated by empirical mode decomposition (EMD) method. The experimental result shows that the number of meaningful decomposed components and frequency resolutions by using HVD method are better than those by using EMD. Such results also reveal the HVD method is superior to the normal EMD method, especially for low frequency narrow band bio-signals such second heart sound, thereby facilitating generating discriminant features for model learning.

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References

  1. Debbal, S.M., Bereksi, R.F.: Time-frequency analysis of the first and the second heartbeat sounds. Appl. Math. Comput. 184(2), 1041–1052 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  2. Chen, T.H., Han, L.Q., Xing, S.X., Wang, H.R., Wang, K.P.: STFT-based comparative studies of heart sound signals. In: 3rd International Conference on Bioinformatics and Biomedical Engineering, Beijing, China (2009)

    Google Scholar 

  3. Ergena, B., Tatara, Y., Gulcur, H.O.: Time-frequency analysis of phonocardiogram signals using wavelet transform: a comparative study. Comput. Meth. BioMech Biomed. Eng. 15(4), 371–381 (2011)

    Article  Google Scholar 

  4. Meziani, F., Debbal, S.M., Atbi, A.: Analysis of phonocardiogram signals using wavelet transform. J. Med. Eng. Tech. 36(6), 283–302 (2012)

    Article  Google Scholar 

  5. Zhao, Z., Zhao, Z., Chen, Y.: Time-frequency analysis of heart sound based on HHT. In: IEEE International Conference on Communications, Circuits and Systems, Hong Kong (2005)

    Google Scholar 

  6. Charleston, V.S., Aljama, C.A., Gonzalez, C.R.: Analysis of simulated heart sounds by intrinsic mode functions. In: 28th IEEE Annual International Conference on Engineering in Medicine and Biology Society, New York, pp. 2848–2851 (2006)

    Google Scholar 

  7. Huang, N.E., Shen, Z., Long, S.R., Wu, M.C., Shih, H.H., Zheng, Q., Yen, N.C., Tung, C.C., Liu, H.H.: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. Roy. Soc. London Series A: Math. Phys. Eng. Sci. 454, 903–995 (1998)

    Article  MATH  MathSciNet  Google Scholar 

  8. Tzu, H.H., Chia, C.C., Wai, C.F., Li, A.H.T., Yu, C.C., Bai, K.H., Yio, W.S.: Time-frequency analysis of heart sound signals based on Hilbert-Huang transformation. In: 16th IEEE International Symposium on Consumer Electronics Pennsylvania, pp. 1–3 (2012)

    Google Scholar 

  9. Ru, Z., Li, X., Chen, F., Yang, L., Li, X.: Research and simulation of speech enhancement algorithm based on HHT. Comput. Tech. Dev. 8, 31 (2010)

    Google Scholar 

  10. Lihan, L., Haibin, W., Yan, W., Ting, T., Xiaochen, W.: Feature analysis of heart sound based on the improved Hilbert-Huang transform. In: 3rd IEEE International Conference on Computer Science and Information Technology, Beijing, China, pp. 378–381 (2010)

    Google Scholar 

  11. Charleston, V.S., Aljama, C.A.T., Gonzalez, C. R.: Analysis of simulated heart sounds by intrinsic mode functions. In: 28th IEEE Annual International Conference on Engineering in Medicine and Biology Society, New York, pp. 2848–2851 (2006)

    Google Scholar 

  12. Boutana, D., Benidir, M., Barkat, B.: On the selection of intrinsic mode function in EMD method: application on heart sound signal. In: 3rd International Symposium on Applied Sciences in Biomedical and Communication Technologies, Roma, Italy, pp. 1–5 (2010)

    Google Scholar 

  13. Cong, Z., Chetouani, M.: Hilbert-Huang transform based physiological signals analysis for emotion recognition. In: IEEE International Symposium on Signal Processing and Information Technology, Ajaman, UAE, pp. 334–339 (2009)

    Google Scholar 

  14. Zhiyuan, S., Qiang, W., Yi, S., Jing J., Yurong, L.: Accent extraction of emotional speech based on modified ensemble empirical mode decomposition. In: IEEE Instrumentation and Measurement Technology Conference, Austin, pp. 600–604 (2010)

    Google Scholar 

  15. Petrantonakis, P.C., Hadjileontiadis, L.J.: Emotion recognition from brain signals using hybrid adaptive filtering and higher order crossings analysis. IEEE Trans. Affectiv. Comput. 1(2), 81–97 (2010)

    Article  Google Scholar 

  16. Zhang, W., Zhang, X., Sun, Y.: Based on EEMD-HHT marginal spectrum of speech emotion recognition. In: IEEE International Conference on Computing, Measurement, Control and Sensor Network, Taiyuan, Chaina, pp. 91–94 (2012)

    Google Scholar 

  17. Feldman, M.: Time-varying vibration decomposition and analysis based on the Hilbert transform. J. Sound Vib. 295, 518–530 (2006)

    Article  MATH  Google Scholar 

  18. Xu, J., Durand, L.G., Pibarot, P.: Nonlinear transient chirp signal modeling of the aortic and pulmonary components of the second heart sound. IEEE Trans. Biomed. Eng. 47(7), 1328–1335 (2000)

    Google Scholar 

  19. Xu, J., Durand, L.G., Pibarot, P.: Nonlinear transient chirp signal modeling of the aortic and pulmonary components of the second heart sound. IEEE Trans. Biomed. Eng. 47(10), 1328–1335 (2000)

    Article  Google Scholar 

  20. Rangayyan, R.M.: http://www2.enel.ucalgary.ca/People/Ranga/enel563/

  21. Feldman, M.: Theoretical analysis and comparison of the Hilbert transform decomposition methods. Mech. Syst. Signal Process. 22(3), 509–519 (2008)

    Article  Google Scholar 

  22. Wu, Z., Huang, N.E.: Ensemble empirical mode decomposition: a noise-assisted data analysis method. Adv. Adpt. Data Anal. 1, 1–41 (2009)

    Article  Google Scholar 

  23. Yeh, J.R., Shieh, J.S., Huang, N.E.: Complementary ensemble empirical mode decomposition: a novel noise enhanced data analysis method. Adv. Adpt. Data Anal. 2, 135–156 (2010)

    Article  MathSciNet  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan

    Shovan Barma, Bo-Wei Chen, Hung-Ming Wang, Hung-Jui Wang & Jhing-Fa Wang

  2. Department of Digital Multimedia Design, Tajen University, Yanpu Township, Pingtung County, Taiwan

    Jhing-Fa Wang

Authors
  1. Shovan Barma
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  2. Bo-Wei Chen
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  3. Hung-Ming Wang
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  4. Hung-Jui Wang
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  5. Jhing-Fa Wang
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Corresponding author

Correspondence to Shovan Barma .

Editor information

Editors and Affiliations

  1. Dickson Computer Systems, Kowloon, Hong Kong SAR

    Dickson K. W. Chiu

  2. The University of Hong Kong, Pokfulam, Hong Kong SAR

    Minhong Wang

  3. University of Craiova, Craiova, Romania

    Elvira Popescu

  4. City University of Hong Kong, Hong Kong, Hong Kong SAR

    Qing Li

  5. City University of Hong Kong, Kowloon, Hong Kong SAR

    Rynson Lau

  6. Department of Computer Science and Information Engineering, National Central University, Jhongli City, Taiwan

    Timothy K. Shih

  7. Department of Electrical Engineering, National Cheng Kung University, Taiwan, Taiwan

    Chu-Sing Yang

  8. Digital Systems Centre for Research & Technology Hellas (CERTH), University of Piraeus Dept of Digital Systems, Piraeus, Greece

    Demetrios G. Sampson

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Barma, S., Chen, BW., Wang, HM., Wang, HJ., Wang, JF. (2015). Second Heart Sound (S2) Decomposition by Hilbert Vibration Decomposition (HVD) for Affective Signal Modeling and Learning. In: Chiu, D., et al. Advances in Web-Based Learning – ICWL 2013 Workshops. ICWL 2013. Lecture Notes in Computer Science(), vol 8390. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-46315-4_23

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  • DOI: https://doi.org/10.1007/978-3-662-46315-4_23

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-46314-7

  • Online ISBN: 978-3-662-46315-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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