Classification of cardiac arrhythmias using deep learning

  • Authors

    • Jeong Hwan Kim
    • Jeong Whan Lee
    • Kyeong Seop Kim
    2018-06-08
    https://doi.org/10.14419/ijet.v7i2.33.14195
  • Electrocardiogram (ECG), Cardiac Arrhythmias, Deep Learning (DL), Fully Connected (FC), R Peak.

  • Abstract

    Background/Objectives: The main objective of this research is to design Deep Learning (DL) architecture to classify an electrocardiogram (ECG) signal into normal sinus rhythm (NSR), premature ventricular contraction (PVC), atrial premature contraction (APC) or right/left bundle branch block (RBBB/LBBB) arrhythmia by empirically optimizing the numbers of hidden layers, the number of neurons in each hidden layer and the number of neurons in input layer in DL model.

    Methods/Statistical analysis: For our experimental simulations, PhysioBank-MIT/BIH annotated ECG database was considered to classify heart beats into abnormal rhythms (PVC, APC, RBBB, LBBB) or normal sinus. The performance of classifying ECG beats by the proposed DL architecture was evaluated by computing the overall accuracy of classifying NSR or four different arrhythmias.

    Findings: Base on testing MIT/BIH arrhythmia database, the proposed DL model can classify the heart rhythm into one of NSR, PVC, APC, RBBB or LBBB beat with the mean accuracy of 95.5% by implementing DL architecture with 200 neurons in input layer, 100 neurons in the first and second hidden layer, respectively and 80 neurons in the 3rd hidden layer.

    Improvements/Applications: Our experimental results show that the proposed DL model might not be quite accurate for detecting APC beats due to its morphological resemblance of NSR. Therefore, we might need to design more sophisticated DL architecture by including more temporal characteristics of APC to increase the classification accuracy of APC arrhythmia in the future research efforts.

     

  • References

    1. [1] Conway, J.C.D., Raposo C.A., Contreras, S.D., and Belchior, J.C. (2014). Identification of Premature Ventricular Contraction (PVC) Caused by Disturbances in Calcium and Potassium Ion Concentrations Using Artificial Neural Networks, Health, 6, 1322-1332.

      [2] Gothwal, H., Kedawat, S., and Kumar, R. (2011). Cardiac Arrhythmias Detection in an ECG Beat Signal Using Fast Fourier Transform and Artificial Neural Network. J. Biomedical Science and Engineering, 4, 289-296.

      [3] Karsoliya, S. (2012). Approximating Number of Hidden layer Neurons in Multiple Hidden Layer BPNN Architecture. International Journal of Engineering Trend and Technology, 3(6), 714-717.

      [4] Pitambare, D. (2016). Survey on Optimization of Number of Hidden Layers in Neural Networks. International Journal of Advanced Research in Computer and Communications Engineering, 5(11), 537-540.

      [5] Kiranyaz, S., Ince, T., Gabboui, M. (2016). Real-Time Patient-Specific ECG Classification by 1-D Convolutional Neural Networks, IEEE Transaction on Biomedical Engineering, 63(3).

      [6] Acharya, U.R., Suri, J.S., Spaan, J.A.E., Krishnan, S.M. (2007). Advances in Cardiac Signal Processing, Springer.

      [7] Gnecchi, J. A. G., Magana, R. M., Espinoza, D. L., Anguiano, A. C. T., Archundia, E. R., Patino, A. M., Miranda, R. C. (2017). DSP-based Arrhythmia Classification Using Wavelet Transform and Probabilistic Neural Network, Biomedical Signal Processing and Control, 32. 44-56.

      [8] He, L., Hou, W., Zhen, X., Peng, C. (2006). Recognition of ECG Patterns Using Artificial Neural Network, International Conference on Intelligent Systems Design and Applications.

      [9] Goldberg, A.L., Amaral, L.A.N., Glass, L., Hausdorff, J.M., Ivanov, P.C.H., Mark, R.G., Mietus, J.E., Moody, G.B., Peng, C.K., Stanley, H.E. (2000).PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation, 101, 215-220.

      [10] Kingma, D. P., Lei Ba, J. (2015). Adam: A Method for Stochastic Optimization, International Conference on Learning Representations.

      [11] Ioffe, S., Szegedy, C. (2015), Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, International Conference on Machine Learning, 37.

      [12] Srivastava, N., Hinto, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R. (2014), Dropout: A Simple Way to Prevent Neural Networks from Overfitting, Journal of Machine Learning Research, 15, 1929-1958.

  • Downloads

  • How to Cite

    Hwan Kim, J., Whan Lee, J., & Seop Kim, K. (2018). Classification of cardiac arrhythmias using deep learning. International Journal of Engineering & Technology, 7(2.33), 401-404. https://doi.org/10.14419/ijet.v7i2.33.14195

    Received date: 2018-06-17

    Accepted date: 2018-06-17

    Published date: 2018-06-08