Arrhythmia detection using deep learning

  • Authors

    • Niharika Pattanaik
    • Ipsita Mohapatra
    • Mihir Narayan Mohanty
    2018-06-08
    https://doi.org/10.14419/ijet.v7i2.33.15524
  • ECG Signal, Cardiac Heart Failure, Discrete Wavelet Transform (DWT), R-Peaks, Deep Neural Network

  • Currently the demand of hospital services increasing gradually. The smart service to the patients is highly essential that counts the death rate. Cardiac problem is a vital problem and people with Cardiological problem are surviving less. The diagnosis of the heart disease facilitates to storage our data. It motivates the application of Data Mining techniques are useful in Health sectors. In this paper authors have taken an approach to detect Arrhythmia using Wavelet transform and Deep Neural Network (DNN). In first stage R-peaks of Arrhythmia data has been detected using Wavelet Transform. In the next stage the Wavelet coefficients are consider as the input features to the DNN model. The classification result for the arrhythmia detection has been presented in the result section of the paper.

     

     


     

  • References

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  • How to Cite

    Pattanaik, N., Mohapatra, I., & Narayan Mohanty, M. (2018). Arrhythmia detection using deep learning. International Journal of Engineering & Technology, 7(2.33), 668-888. https://doi.org/10.14419/ijet.v7i2.33.15524