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.

  • 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

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  • 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