Skip to main content
SpringerLink
Log in

Detecting Ventricular Fibrillation and Ventricular Tachycardia for Small Samples Based on EMD and Symbol Entropy

  • Conference paper
  • First Online:
Intelligent Computing Theories and Application (ICIC 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9771))

Included in the following conference series:

Abstract

In this paper, we proposed a new method based on Symbol Entropy and Empirical Mode Decomposition (EMD) to detect ventricular fibrillation (VF) and ventricular tachycardia (VT). Initially, we applied the EMD to decompose VF and VT signals into five sub-bands respectively. And then, we calculated the Symbol Entropy of each sub-bans as the feature to detect VT and VF. We employed the public data set to assess the proposed method. Experimental results showed that, using classification of support vector machine (SVM), the proposed method can successfully distinguish VF from VT with the classification accuracy up to 100 % based on small samples. The duration of each sample was 2 s. Moreover, the classification accuracy of the proposed method is far higher than the classification accuracy of the original signals using Symbol Entropy directly.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Othman, M.A., Safri, N.M., Ghani, I.A.: A new semantic mining approach for detecting ventricular tachycardia and ventricular fibrillation. Biomed. Sig. Process. Control 8(2), 222–227 (2013)

    Article  Google Scholar 

  2. Kong, D.R., Xie, H.B.: Use of modified sample entropy measurement to classify ventricular tachycardia and fibrillation. Measurement 44(3), 653–662 (2011)

    Article  Google Scholar 

  3. Thakor, N.V., Zhu, Y.S., Pan, K.Y.: Ventricular tachycardia and fibrillation detection by a sequential hypothesis testing algorithm. IEEE Trans. Biomed. Eng. 37(9), 837–843 (1990)

    Article  Google Scholar 

  4. Zhang, X.S., Zhu, Y.S., Thakor, N.V., Wang, Z.Z.: Detecting ventricular tachycardia and fibrillation by complexity measure. IEEE Trans. Biomed. Eng. 46(5), 548–555 (1999)

    Article  Google Scholar 

  5. Zhang, H.X., Zhu, Y.S., Xu, Y.H.: Complexity information based analysis of pathological ECG rhythm for ventricular tachycardia and ventricular fibrillation. Int. J. Bifurc. Chaos 12(10), 2293–2303 (2002)

    Article  Google Scholar 

  6. Zhang, H.X., Zhu, Y.S.: Qualitative chaos analysis for ventricular tachycardia and fibrillation based on symbol complexity. Med. Eng. Phys. 23(8), 523–528 (2001)

    Article  Google Scholar 

  7. Owis, M.I., Abou-Zied, A.H., Youssef, A.B.M.: Study of features based on nonlinear dynamical modeling in ECG arrhythmia detection and classification. IEEE Trans. Biomed. Eng. 49(7), 733–736 (2002)

    Article  Google Scholar 

  8. Fleisher, L.A., Pincus, S.M.S., Rosenbaum, H.: Approximate entropy of heart rate as a correlate of postoperative ventricular dysfunction. Anesthesiology 78(4), 683–692 (1993)

    Article  Google Scholar 

  9. Tong, S., Bezerianos, A., Paul, J., Thakor, N.: Nonextensive entropy measure of EEG following brain injury from cardiac arrest. Phys. A 305(3), 619–628 (2002)

    Article  MATH  Google Scholar 

  10. Gamero, L.G., Plastino, A., Torres, M.E.: wavelet analysis and nonlinear dynamics in a non extensive setting. Phys. A 246(3), 487–509 (1997)

    Article  Google Scholar 

  11. Tsallis, C.: Possible generalization of Boltzmann-Gibbs statistics. J. Stat. Phys. 52, 479–487 (1988)

    Article  MathSciNet  MATH  Google Scholar 

  12. Sherman, L.D., Callaway, C.W., Menegazzi, J.J.: Ventricular fibrillation exhibits dynamical properties and self-similarity. Resuscitation 47(2), 163–173 (2000)

    Article  Google Scholar 

  13. Wang, J., Chen, J.: Symbol dynamics of ventricular tachycardia and ventricular fibrillation. Phys. A 389(10), 2096–2100 (2010)

    Article  Google Scholar 

  14. Owis, M.I., Abou-Zied, A.H., Youssef, A.B.M., Kadah, Y.M.: Study of features based on nonlinear dynamical modeling in ECG arrhythmia detection and classification. IEEE Trans. Biomed. Eng. 49, 733–736 (2002)

    Article  Google Scholar 

  15. Pachori, R.B., et al.: Analysis of normal and epileptic seizure EEG signals using empirical mode decomposition. Comput. Methods Programs Biomed. 104, 373–381 (2011)

    Article  Google Scholar 

  16. Xia, D., Meng, Q., Chen, Y., Zhang, Z.: Classification of ventricular tachycardia and fibrillation based on the Lempel-Ziv complexity and EMD. In: Huang, D.-S., Han, K., Gromiha, M. (eds.) ICIC 2014. LNCS, vol. 8590, pp. 322–329. Springer, Heidelberg (2014)

    Google Scholar 

Download references

Acknowledgments

This work was supported by the National Natural Science Foundation of China (Grant Nos. 61201428, 61302128), the Natural Science Foundation of Shandong Province, China (Grant Nos. ZR2010FQ020, ZR2013FL002), the Shandong Distinguished Middle-aged and Young Scientist Encourage and Reward Foundation, China (Grant Nos. BS2009SW003, BS2014DX015).

Author information

Authors and Affiliations

  1. School of Information Science and Engineering, University of Jinan, Jinan, 250022, China

    Yingda Wei, Qingfang Meng & Dong Wang

  2. Shandong Provincial Key Laboratory of Network Based Intelligent Computing, Jinan, 250022, China

    Yingda Wei & Qingfang Meng

  3. Institute of Jinan Semiconductor Elements Experimentation, Jinan, 250014, China

    Qiang Zhang

Authors
  1. Yingda Wei
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Qingfang Meng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Qiang Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Dong Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qingfang Meng .

Editor information

Editors and Affiliations

  1. Tongji University , Shanghai, China

    De-Shuang Huang

  2. Polytechnic of Bari , Bari, Italy

    Vitoantonio Bevilacqua

  3. University of Wollongong , North Wollongong, New South Wales, Australia

    Prashan Premaratne

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Wei, Y., Meng, Q., Zhang, Q., Wang, D. (2016). Detecting Ventricular Fibrillation and Ventricular Tachycardia for Small Samples Based on EMD and Symbol Entropy. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Application. ICIC 2016. Lecture Notes in Computer Science(), vol 9771. Springer, Cham. https://doi.org/10.1007/978-3-319-42291-6_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-42291-6_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-42290-9

  • Online ISBN: 978-3-319-42291-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation