Electronic Science and Technology ›› 2021, Vol. 34 ›› Issue (5): 42-46.doi: 10.16180/j.cnki.issn1007-7820.2021.05.008

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ECG Classification Based on Bispectrum and Spectral Features

LIU Shu,SHAO Jie,ZHANG Yiting,ZHANG Shanzhang   

  1. College of Electronic and Information Engineering,Nanjing University of Aeronautics and Astronautics, Nanjing 211106,China
  • Online:2021-05-15 Published:2021-05-24
  • Supported by:
    Open Research Fund of Key Laboratory of Ministry of Education(UASP1604)


As the lowest high-order spectrum, bispectrum has many excellent characteristics, but it also has the defect of large calculation. Based on bispectrum matrix, a feature extraction method for two-dimensional ECG signal is proposed in the present study. The spectral flatness, spectral brightness and spectral roll off are combined to form the eigenvector, which is further combined with the support vector machine classification method founded on radial basis kernel function to realize the classification and recognition of ECG signals. The ECG signal in MIT-BIH database is applied to verify the method. Experimental result show that the two-dimensional spectrum feature extraction method based on bispectrum proposed in this study has a small amount of computation and an accuracy of 93.4%, indicating that the proposed method can effectively realize the diagnosis of arrhythmia and the classification of ECG signals.

Key words: ECG signals, feature extraction, bispectrum analysis, spectrum flatness, spectral brightness, spectral roll-off, classification recognition, support vector machine

CLC Number: 

  • TP274