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)


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%.


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


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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

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