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 shortterm 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. Cardiovascular diseases are the leading cause of death throughout the world. Approximately 32.1% of the annual global deaths reported in 2015 were related with cardiovascular diseases 1 . Due to increases in work stress and psychological issues, the incidences of cardiovascular diseases have kept growing among young people in recent years. As an effective method, Electrocardiogram (ECG) tests, which provide a diagnostic technique for recording the electrophysiological activity of the heart over time through the chest cavity via electrodes placed on the skin 2 , have been used to help doctors diagnose heart diseases. However, as vast volumes of ECG data are generated each day and continuously over 24-hour periods 3 , it is really difficult to manually analyze these data, which calls for automatic techniques to support the efficient diagnosis of heart diseases. Machine learning is employed frequently as an artificial intelligence technique to facilitate automated analysis. Many machine learning techniques have been applied to medical-aided diagnosis, such as support vector machines . However, most of these methods require large amounts of labeled data for training the model, which is an empirical problem that still needs to be solved. For example, large volumes of labeled ECG data are usually required as training samples for heart disease classification systems. Moreover, when machine learning approaches are applied to personalized medicine research, such as personalized heart disease research, the ECGs are often categorized based on the personal features of the patients, such as their gender and age. Thus, the problems caused by lacking of good ECG data are exacerbated before any subsequent analysis. Furthermore, maintaining the privacy of patients is always an issue that cannot be igored. However, the personal information and private clinical data obtained from patients are still likely to be illegally leaked. An optimal solution is to generate synthetic data without any private details to satisfy the requirements for research.
The most recent attempt to generate synthetic ECG used bidirectional LSTMs in the generator and convolutional neural networks in the discriminator REF .
Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network
Scientific Reports
Scientific Reports
Computer Science Medicine