Improving ECG Classification Using Generative Adversarial Networks


  • Tomer Golany Technion - Israel Institute of Technology
  • Gal Lavee eBay Research
  • Shai Tejman Yarden Sheba Medical Center
  • Kira Radinsky Technion - Israel Institute of Technology



The Electrocardiogram (ECG) is performed routinely by medical personell to identify structural, functional and electrical cardiac events. Many attempts were made to automate this task using machine learning algorithms. Numerous supervised learning algorithms were proposed, requiring manual feature extraction. Lately, deep neural networks were also proposed for this task for reaching state-of-the-art results. The ECG signal conveys the specific electrical cardiac activity of each subject thus extreme variations are observed between patients. These variations and the low amount of training data available for each arrhythmia are challenging for deep learning algorithms, and impede generalization. In this work, the use of generative adversarial networks is studied for the synthesis of ECG signals, which can then be used as additional training data to improve the classifier performance. Empirical results prove that the generated signals significantly improve ECG classification.




How to Cite

Golany, T., Lavee, G., Tejman Yarden, S., & Radinsky, K. (2020). Improving ECG Classification Using Generative Adversarial Networks. Proceedings of the AAAI Conference on Artificial Intelligence, 34(08), 13280-13285.



IAAI Technical Track: Emerging Papers