Key result
Diffusion model data augmentation improves AF detection and classification compared to generative adversarial networks.
Why the study?
Deep learning models are used to detect atrial fibrillation from ECG signals, but using diffusion models for sample augmentation to improve detection performance needed exploration.
Does data augmentation with a diffusion model improve the performance of deep learning-based AF detection from ECG signals compared to GAN?
Does data augmentation with a diffusion model improve the performance of deep learning-based AF detection from ECG signals compared to GAN?
Diffusion models offer a superior data augmentation strategy compared to GANs for improving deep learning-based atrial fibrillation detection from ECGs.
Answers are grounded in this paper and cite their sources.
May enhance synthetic ECG data for AF detection models; leaves open clinical validation and outcome impact.
Atrial Fibrillation (AF) is the most common type of cardiac arrhythmia, posing significant health risks such as strokes and cardiac failure. Deep learning methods have been employed to automatically diagnose AF from electrocardiogram (ECG) signals. In this study, transforming the ECG signal into two-dimensional Poincaré plots, we explore the application of diffusion model in the augmentation of AF samples to improve the performance of AF detection via deep learning. In this regard, we compare the performance of diffusion model and generative adversarial network (GAN). Our results suggest that diffusion model can potentially generate more satisfactory samples in terms of both the FID score and the downstream classification task.
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Vashagh et al. (2023) studied Atrial Fibrillation. Diffusion model for data augmentation vs. Generative adversarial network (GAN) was evaluated on FID score and downstream classification task performance. Data augmentation using a diffusion model generated more satisfactory samples for atrial fibrillation detection compared to a generative adversarial network, improving FID scores and classification.
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