Why the study?
There is a demand for extensive ECG datasets, motivating the development of generative models to artificially augment databases of real ECGs.
Does a 2D BiLSTM GAN model successfully generate synthetic standard 12-lead ECGs that mimic specific clinical conditions?
Does a 2D BiLSTM GAN model successfully generate synthetic standard 12-lead ECGs that mimic specific clinical conditions?
A 2D BiLSTM GAN can successfully generate diverse and physiologically plausible synthetic 12-lead ECGs, offering a method to augment datasets for training deep learning algorithms.
Synthetic ECG generation needs clinical validation before any use; leaves open its value for expanding scarce training datasets in ML research.
This paper proposes a two-dimensional (2D) bidirectional long short-term memory generative adversarial network (GAN) to produce synthetic standard 12-lead ECGs corresponding to four types of signals: left ventricular hypertrophy (LVH), left branch bundle block (LBBB), acute myocardial infarction (ACUTMI), and Normal. It uses a fully automatic end-to-end process to generate and verify the synthetic ECGs that does not require any visual inspection. The proposed model is able to produce synthetic standard 12-lead ECG signals with success rates of 98% for LVH, 93% for LBBB, 79% for ACUTMI, and 59% for Normal. Statistical evaluation of the data confirms that the synthetic ECGs are not biased towards or overfitted to the training ECGs, and span a wide range of morphological features. This study demonstrates that it is feasible to use a 2D GAN to produce standard 12-lead ECGs suitable to augment artificially a diverse database of real ECGs, thus providing a possible solution to the demand for extensive ECG datasets.
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Zhang et al. (2021) studied this question.
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