Skip to main content
SpringerLink
Log in

A Method Using the Lempel-Ziv Complexity to Detect Ventricular Tachycardia and Fibrillation

  • Conference paper
  • First Online:
Advances in Neural Networks - ISNN 2017 (ISNN 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10262))

Included in the following conference series:

  • 2759 Accesses

Abstract

This paper use the Lempel-Ziv complexity to automatically detect ventricular fibrillation (VF) and ventricular tachycardia (VT) based on Wavelet transform (WT) and empirical mode decomposition (EMD). We respectively select WT and EMD to decompose original signals into different sub-bands. Electrocardiogram (ECG) signals were first decomposed into five sub-bands based on Wavelet transform and EMD. Then the complexity of each sub-band was used as a feature to detect VF and VT. A public dataset was utilized. Experimental results show the new method can distinguish VT from VF with the accuracy up to 99.50%.

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 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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. Small, M., Simonotto, J., et al.: Uncovering non-linear structure in human ECG recordings. Chaos. Solitons. Fract. 13, 1755–1762 (2002)

    Article  Google Scholar 

  2. Chen, S., et al.: Ventricular fibrillation detection by a regression test on the autocorrelation function. Med. Biol. Eng. Comput. 25(3), 241–249 (1987)

    Article  Google Scholar 

  3. Thakor, N.V., et al.: Ventricular tachycardia and fibrillation detection by a sequential hypothesis testing algorithm. IEEE Trans. Biomed. Eng. 37, 837–843 (1990)

    Article  Google Scholar 

  4. Alonso-Atienza, F., Rojo-álvarez, J.L., Rosado-Munoz, A., Vinagre, J.J., García-Alberola, A., Camps-Valls, G.: Feature selection using support vector machines and bootstrap methods for ventricular fibrillation detection. Expert Syst. Appl. 39, 1956–1967 (2012)

    Article  Google Scholar 

  5. Li, S., Zhou, S., et al.: Feature extraction and recognition of ictal EEG using EMD and SVM. Comput. Biol. Med. 43, 807–816 (2013)

    Article  Google Scholar 

  6. Richman, J.S., Moorman, J.R.: Physiological time-series analysis using approximate and sample entropy. Am. J. Phys. – Heart Circ. Physiol. 278, H2039–H2049 (2000)

    Google Scholar 

  7. Gómeza, C., Hornero, R., Abásolo, D., Fernández, A., López, M.: Complexity analysis of the magnetoencephalogram background activity in Alzheimer’s disease patients. Med. Eng. Phys. 28, 851–859 (2006)

    Article  Google Scholar 

  8. Owis, M.I., et al.: 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 

  9. Fernández, A., Gómez, C., Hornero, R., López-Ibor, J.J.: Complexity and schizophrenia. Prog. Neuro-Psychopharmacol. 45, 267–276 (2013)

    Article  Google Scholar 

  10. Abáasolo, D., Hornero, R., Gómez, C., García, M., López, M.: Analysis of EEG background activity in Alzheimer’s disease patients with Lempel-Ziv complexity and central tendency measure. Med. Eng. Phys. 28, 315–322 (2006)

    Article  Google Scholar 

  11. Sarlabous, L., Torres, A., Fiz, J.A., Morera, J., Jané, R.: Index for estimation of muscle force from mechanomyography based on the Lempel-Ziv algorithm. J. Electromyogr. Kinesiol. 23, 548–557 (2013)

    Article  Google Scholar 

  12. Small, M., et al.: Deterministic nonlinearity in ventricular fibrillation. Chaos 10, 268–277 (2000)

    Article  MATH  Google Scholar 

  13. Aboy, M., Hornero, R., Abásolo, D., Álvarez, D.: Interpretation of the Lempel-Ziv complexity measure in the context of biomedical signal analysis. IEEE Trans. Biomed. Eng. 53(11), 2282–2288 (2006)

    Article  Google Scholar 

  14. Khan, Y.U., Gotman, J.: Wavelet based automatic seizure detection in intracerebral electroencephalogram. Clin. Neurophysiol. 114(5), 898–908 (2003)

    Article  Google Scholar 

  15. Xie, H., Gao, Z., et al.: Classification of ventricular tachycardia and fibrillation using fuzzy similarity-based approximate entropy. Expert Syst. Appl. 38, 3973–3981 (2011)

    Article  Google Scholar 

  16. Pachori, B.R., 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 

  17. Burrus, C.S., Gopinath, R.A., et al.: Introduction to Wavelets and Wavelet Transforms: A Primer. Prentice-Hall, Upper Saddle River (1998)

    Google Scholar 

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

    Article  Google Scholar 

  19. Wang, J., Chiang, W., et al.: ECG arrhythmia classification using a probabilistic neural network with a feature reduction method. Neurocomputing 116, 38–45 (2013)

    Article  Google Scholar 

  20. Huang, N.E., et al.: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. R. Soc. Lond. 454, 903–995 (1998)

    Article  MathSciNet  MATH  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant No. 61201428, 61302090), the Natural Science Foundation of Shandong Province, China (Grant No. ZR2010FQ020, ZR2013FL002).

Author information

Authors and Affiliations

  1. The Information Office, Liaocheng Vocational and Technical College, Liaocheng, 252000, China

    Deling Xia & Yuetian Li

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

    Qingfang Meng

  3. The Basic Department, Liaocheng Vocational and Technical College, Liaocheng, 252000, China

    Jie He

Authors
  1. Deling Xia
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yuetian Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Qingfang Meng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jie He
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Deling Xia .

Editor information

Editors and Affiliations

  1. Dalian University of Technology, Dalian, China

    Fengyu Cong

  2. City University of Hong Kong, Kowloon Tong, Hong Kong

    Andrew Leung

  3. Chinese Academy of Sciences, Beijing, China

    Qinglai Wei

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Xia, D., Li, Y., Meng, Q., He, J. (2017). A Method Using the Lempel-Ziv Complexity to Detect Ventricular Tachycardia and Fibrillation. In: Cong, F., Leung, A., Wei, Q. (eds) Advances in Neural Networks - ISNN 2017. ISNN 2017. Lecture Notes in Computer Science(), vol 10262. Springer, Cham. https://doi.org/10.1007/978-3-319-59081-3_19

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-59081-3_19

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59080-6

  • Online ISBN: 978-3-319-59081-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation