Key result
Deep learning-generated synthetic 12-lead ECGs achieve ~93% classification accuracy on real-world datasets.
Why the study?
The study was conducted to construct a synthetic ECG signal dataset to overcome personal information sensitivity and complex disclosure policies that restrict medical data sharing for artificial intelligence research.
A deep learning-generated synthetic 12-lead ECG dataset closely mimics real-world ECGs and achieves high accuracy in diagnostic classification models, providing a viable, privacy-preserving alternative for AI research.
Supports synthetic ECG data for privacy-preserving AI training; leaves open clinical adoption pending prospective validation.
Purpose: The purpose of this study is to construct a synthetic dataset of ECG signal that overcomes the sensitivity of personal information and the complexity of disclosure policies. Methods: The public dataset was constructed by generating synthetic data based on the deep learning model using a convolution neural network (CNN) and bi-directional long short-term memory (Bi-LSTM), and the effectiveness of the dataset was verified by developing classification models for ECG diagnoses. Results: The synthetic 12-lead ECG dataset generated consists of a total of 6000 ECGs, with normal and 5 abnormal groups. The synthetic ECG signal has a waveform pattern similar to the original ECG signal, the average RMSE between the two signals is 0.042 µV, and the average cosine similarity is 0.993. In addition, five classification models were developed to verify the effect of the synthetic dataset and showed performance similar to that of the model made with the actual dataset. In particular, even when the real dataset was applied as a test set to the classification model trained with the synthetic dataset, the classification performance of all models showed high accuracy (average accuracy 93.41%). Conclusion: The synthetic 12-lead ECG dataset was confirmed to perform similarly to the real-world 12-lead ECG in the classification model. This implies that a synthetic dataset can perform similarly to a real dataset in clinical research using AI. The synthetic dataset generation process in this study provides a way to overcome the medical data disclosure challenges constrained by privacy rights, a way to encourage open data policies, and contribute significantly to promoting cardiovascular disease research.
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Yoo et al. (2023) studied Cardiovascular disease (ECG abnormalities) (n=6,000). Synthetic 12-lead ECG dataset generation (CNN and Bi-LSTM) vs. Real-world 12-lead ECG dataset was evaluated on Average classification accuracy of models trained with synthetic dataset. A deep learning-generated synthetic 12-lead ECG dataset demonstrated high similarity to real-world signals and achieved an average classification accuracy of 93.41% when tested on real datasets.
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