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Sci Rep. 2019 May 1;9(1):6734. doi: 10.1038/s41598-019-42516-z.

Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network.

Zhu F1,2, Ye F1, Fu Y3, Liu Q1, Shen B4.

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School of Computer Science and Technology, Soochow University, Suzhou, 215006, China.
Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, Suzhou, 215006, China.
School of Computer Science and Engineering, Changshu Institute of Technology, Changshu, 215500, China.
Institutes for Systems Genetics, West China Hospital, Sichuan University, Chengdu, 610041, China.


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 retained. The model includes a generator and a discriminator, where the generator employs the two layers of the BiLSTM networks and the discriminator is based on convolutional neural networks. The 48 ECG records from individuals of the MIT-BIH database were used to train the model. We compared the performance of our model with two other generative models, the recurrent neural network autoencoder(RNN-AE) and the recurrent neural network variational autoencoder (RNN-VAE). The results showed that the loss function of our model converged to zero the fastest. We also evaluated the loss of the discriminator of GANs with different combinations of generator and discriminator. The results indicated that BiLSTM-CNN GAN could generate ECG data with high morphological similarity to real ECG recordings.

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