• DocumentCode
    2130046
  • Title

    A robust T-wave alternans detection algorithm based on the wavelet transform and Bootstrap

  • Author

    Lihuang She ; Mingquan Wang ; Hongyan Wang ; Shi Zhang

  • Author_Institution
    Coll. of Inf. Sci. & Eng., Northeastern Univ., Shenyang, China
  • fYear
    2012
  • fDate
    16-18 Oct. 2012
  • Firstpage
    413
  • Lastpage
    417
  • Abstract
    In this article, continuous wavelet and Lipschitz indices are used to divide non-stationary T-wave alternans (TWA) in ECG signal into segments. Then Bootstrap method is applied to test the TWA magnitude. Experiments have extracted ECG signal from the database, and form T-wave alternans signal, Gaussian noise, baseline drift; and took a test to MIT´s TWADB (10 groups) data; and in the last part, we have taken the real ECG to test. The achieved results are satisfactory. It has not only effectively reduced effects on the non-stability of TWA, but also solved the problem that the short-term TWA are difficult to test. The correlation coefficient between measurement and simulation of the magnitude of the true value reached to 0.96.
  • Keywords
    Gaussian noise; bioelectric potentials; electrocardiography; medical signal detection; medical signal processing; statistical analysis; wavelet transforms; Bootstrap method; ECG signal extraction; ECG signal segmentation; Gaussian noise; Lipschitz index; MIT TWADB data; TWA magnitude measurement; TWA magnitude simulation; baseline drift; continuous wavelet transform; correlation coefficient; electrocardiography; nonstationary T-wave alternan detection algorithm; Bootstrap; Lipschitz; T-wave alternans; Wavelet modulus maxima;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Biomedical Engineering and Informatics (BMEI), 2012 5th International Conference on
  • Conference_Location
    Chongqing
  • Print_ISBN
    978-1-4673-1183-0
  • Type

    conf

  • DOI
    10.1109/BMEI.2012.6512878
  • Filename
    6512878