Key result
DS4U-Net diffusion model generates realistic multi-lead ECG signals that outperform other models in classification.
Why the study?
Automated diagnosis requires large amounts of clinical data to train models, but privacy concerns lead to low sample sizes in some ECG dataset categories, necessitating high-quality multi-lead ECG signal generation.
Does the DS4U-Net diffusion model improve the generation of high-quality multi-lead ECG signals compared to other generative models?
Does the DS4U-Net diffusion model improve the generation of high-quality multi-lead ECG signals compared to other generative models?
A novel diffusion model-based approach (DS4U-Net) effectively generates high-quality multi-lead ECG signals that can improve the training of automated diagnostic classifiers.
May augment ECG classifier training datasets; leaves open prospective clinical validation.
The electrocardiogram (ECG) signals are a safe, convenient, and effective method for diagnosing cardiac diseases. However, due to the high privacy of ECG signals, some categories in ECG datasets have low sample sizes. Computerized automated medical-assisted diagnosis typically requires a large amount of clinical data to train models. Therefore, generating high-quality ECG signals is crucial for assisting in the diagnosis of cardiac disease. This paper proposes a method for generating multi-lead ECG signals based on a diffusion model. The method uses a fusion of a structured state-space sequence model and the U-Net network architecture as an additive and denoising model for the diffusion process. The structured state space sequence model solves the problem of capturing long-time sequence information instability and, at the same time, combines with the U-Net to enhance the model's ability to learn complex features. The experiments demonstrate that the signals generated by the model exhibit spectral features similar to those of actual signals. The method generates signals that are used to train a classifier, which is then tested on real data. The results show good classification performance and confirm the effectiveness of the method. Comparison with several other generative models also indicates that this method outperforms them.
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Du et al. (2024) studied Cardiac diseases (ECG signal generation). DS4U-Net (diffusion model-based approach) vs. Other generative models was evaluated on Classification performance and spectral feature similarity. The DS4U-Net diffusion model generated high-quality multi-lead ECG signals with realistic spectral features, yielding good classification performance that outperformed other generative models.
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