Advertisement

Classification of Ventricular Tachycardia and Fibrillation Based on the Lempel-Ziv Complexity and EMD

  • Deling Xia
  • Qingfang Meng
  • Yuehui Chen
  • Zaiguo Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8590)

Abstract

Detection of ventricular tachycardia (VT) and ventricular fibrillation (VF) in electrocardiography (ECG) has clinical research significance. The complexity of the heart signals has changed significantly, when the heart state switches from normal sinus rhythm to VT or VF. With the consideration of the non-stationary of VT and VF, we proposed a novel method for classification of VF and VT in this paper, based on the Lempel-Ziv (LZ) complexity and empirical mode decomposition (EMD). The EMD first decomposed ECG signals into a set of intrinsic mode functions (IMFs). Then the complexity of each IMF was used as a feature in order to discriminate between VF and VT. A public dataset was utilized for evaluating the proposed method. Experimental results showed that the proposed method could successfully distinguish VF from VT with the highest accuracy up to 97.08%.

Keywords

ventricular fibrillation ventricular tachycardia the Lempel-Ziv complexity empirical mode decomposition (EMD) 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Othman, M.A., Safri, N.M., Ghani, I.A., et al.: A New Semantic Mining Approach for Detecting Ventricular Tachycardia and Ventricular Fibrillation. Biomedical Signal Processing and Control 8, 222–227 (2013)CrossRefGoogle Scholar
  2. 2.
    Kong, D.-R., Xie, H.-B.: Use of Modified Sample Entropy Measurement to Classify Ventricular Tachycardia and Fibrillation. Measurement 44, 653–662 (2011)CrossRefGoogle Scholar
  3. 3.
    Lempel, A., Ziv, J.: On The Complexity of Finite Sequences. IEEE Trans. Inform. Theory 22, 75–81 (1976)CrossRefzbMATHMathSciNetGoogle Scholar
  4. 4.
    Kolmogorov, A.N.: Three Approaches to The Quantitative Definition of Information. Inform. Trans. 1, 3–11 (1965)zbMATHMathSciNetGoogle Scholar
  5. 5.
    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)CrossRefGoogle Scholar
  6. 6.
    Small, M., Yu, D., Simonotto, J., Harrison, R.G., Grubb, N., Fox, K.A.A.: Uncovering Non-Linear Structure in Human ECG Recordings. Chaos Solitons Fract. 13, 1755–1762 (2002)CrossRefGoogle Scholar
  7. 7.
    Pincus, S.M.: Approximate Entropy As A Measure of System Complexity. Proc. Natl. Acad. Sci. USA 88, 2297–2301 (1991)CrossRefzbMATHMathSciNetGoogle Scholar
  8. 8.
    Xie, H.B., Gao, Z.M., Liu, H.: Classification of Ventricular Tachycardia and Fibrillation Using Fuzzy Similarity-Based Approximate Entropy. Expert Systems with Applications 38, 3973–3981 (2011)CrossRefGoogle Scholar
  9. 9.
    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)Google Scholar
  10. 10.
    Leonardo, S., Abel, T., JosÉ, A.F., Josep, M., Raimon, J.: Index for Estimation of Muscle Force From Mechanomyography Based on the Lempel-Ziv Algorithm. Journal of Electromyography and Kinesiology 23, 548–557 (2013)CrossRefGoogle Scholar
  11. 11.
    G´Omeza, C., Hornero, R., Ab´Asolo, D., Fern´Andez, A., L´Opez, M.: Complexity Analysis of the Magnetoencephalogram Background Activity in Alzheimer’s Disease Patients. Medical Engineering & Physics 28, 851–859 (2006)CrossRefGoogle Scholar
  12. 12.
    Pachori, R.B., et al.: Analysis of Normal and Epileptic Seizure EEG Signals Using Empirical Mode Decomposition. Computer Methods and Programs in Biomedicine 104, 373–381 (2011)CrossRefGoogle Scholar
  13. 13.
    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, 837–843 (1990)CrossRefGoogle Scholar
  14. 14.
    Li, S.F., Zhou, W.D., Yuan, Q., Geng, S.J., Cai, D.M.: Feature Extraction and Recognition of Ictal EEG Using EMD and SVM. Computers in Biology and Medicine 43, 807–816 (2013)CrossRefGoogle Scholar
  15. 15.
    Zhang, H.X., Zhu, Y.S., Wang, Z.M.: Complexity Measure and Complexity Rate Information Based Detection of Ventricular Tachycardia and Fibrillation. Medical & Biological Engineering &Computing 38, 553–557 (2000)CrossRefGoogle Scholar
  16. 16.
    Chen, S.W.: A Two-Stage Discrimination of Cardiac Arrhythmias Using a Total Least Squares-Based Prony Modeling Algorithm. IEEE Trans. Biomed. Eng. 47, 1317–1327 (2000)CrossRefGoogle Scholar
  17. 17.
    Zhang, H.X., Zhu, Y.S.: Qualitative Chaos Analysis for Ventricular Tachycardia and Fibrillation Based on Symbolic Complexity. Med. Eng. Phys. 23, 523–528 (2001)CrossRefGoogle Scholar
  18. 18.
    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. Bifurcat. Chaos 12(10), 2293–2303 (2002)CrossRefGoogle Scholar
  19. 19.
    Kong, D.R., Xie, H.B.: Use of Modified Sample Entropy Measurement to Classify Ventricular Tachycardia and Fibrillation. Measurement 44, 653–662 (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Deling Xia
    • 1
    • 2
  • Qingfang Meng
    • 1
    • 2
  • Yuehui Chen
    • 1
    • 2
  • Zaiguo Zhang
    • 3
  1. 1.The School of Information Science and EngineeringUniversity of JinanJinanChina
  2. 2.Shandong Provincial Key Laboratory of Network Based Intelligent ComputingJinanChina
  3. 3.CET Shandong Electronics Co., Ltd.JinanChina

Personalised recommendations