Automatic detection and classification of cardiac arrhythmia using neural network

  • Authors

    • N N. S. V Rama Raju gitam university
    • V Malleswara Rao gitam university
    • I Srinivasa Rao gitam university
    2018-07-11
    https://doi.org/10.14419/ijet.v7i3.14084
  • Cardiac Arrhythmias, DTCWT, DWT, MIT-BIH, Neural Network, QRS.

  • This paper proposes a Neural Network classifier model for the automatic identification of the ventricular and supraventricular arrhythmias cardiac arrhythmias. The wavelet transform (DWT) and dual tree complex wavelet transform (DTCWT) is applied for QRS complex detec-tion. After segmentation both feature of DWT and DTCWT is combined for feature extraction, statistical feature has been calculated to re-duce the overhead of classifier. An adaptive filtering with the soft computed wavelet thersholding to the signals before the extraction is done in pre-processing. Different ECG database is considered to evaluate the propose work with MIT-BIH database Normal Sinus Rhythm Da-tabase (NSRD) , and MIT-BIH Supraventricular Arrhythmia Database (svdb) .The evaluated outcomes of ECG classification claims 98 -99 % of accuracy under different training and testing situation.

     

     

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    N. S. V Rama Raju, N., Malleswara Rao, V., & Srinivasa Rao, I. (2018). Automatic detection and classification of cardiac arrhythmia using neural network. International Journal of Engineering & Technology, 7(3), 1482-1490. https://doi.org/10.14419/ijet.v7i3.14084