Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network
By Fei Zhu, Fei Ye, Yuchen Fu, QUAN LIU, Bairong Shen. Scientific Reports, 2019
350 citations recorded by the source corpus.
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.
Subfields: Cardiology and Cardiovascular Medicine, Electrical and Electronic Engineering, Pulmonary and Respiratory Medicine
View Paper: https://www.semanticscholar.org/paper/91ee6164bfbe7a75eaccf79283da54200cb30c93
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In Cardiology and Cardiovascular Medicine
In Electrical and Electronic Engineering
In Pulmonary and Respiratory Medicine
Citations in the corpus
- Generative Adversarial Networks in Time Series: A Systematic Literature Review (2022, background): “Fortunately, we are beginning to see the uptake of GANs applied to time series with these types of medical and physiological data [12, 21, 31, 49, 111].”
- Synthesis of Realistic ECG using Generative Adversarial Networks (2019, methodology): “The models presented in this work follow the architecture of a regular GAN but utilise recurrent neural networks and convolutional neural networks which have shown previous success in synthesising time series data [26, 28].”