Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network

Heart disease is a malignant threat to human health. Electrocardiogram (ECG) tests are used to help diagnose heart disease by recording the heart's activity. However, automated medical-aided diagnosis with computers usually requires a large volume of labeled clinical data without patients' privacy to train the model, which is an empirical problem that still needs to be solved. To address this problem, we propose a generative adversarial network (GAN), which is composed of a bidirectional long short-term memory(LSTM) and convolutional neural network(CNN), referred as BiLSTM-CNN,to generate synthetic ECG data that agree with existing clinical data so that the features of patients with heart disease can be…

• NN
  National Natural Science Foundation of China

  Awards: 61772355, 61303108, 61373094, and 61772355, 61303108, 61373094
• SU
  Soochow University

  Award: KJS1524