Deep learning models need a sufficient amount of data in order to be able to find the hidden patterns in it. It is the purpose of generative modeling to learn the data distribution, thus allowing us to sample more data and augment the original dataset. In the context of physiological data, and more specifically electrocardiogram (ECG) data, given its sensitive nature and expensive data collection, we can exploit the benefits of generative models in order to enlarge existing datasets and improve downstream tasks, in our case, classification of heart rhythm. In this work, we explore the usefulness of synthetic data generated with different generative models from Deep Learning namely Diffweave, Time-Diffusion and Time-VQVAE in order to obtain better classification results for two open source multivariate ECG datasets. Moreover, we also investigate the effects of transfer learning, by fine-tuning a synthetically pre-trained model and then progressively adding increasing proportions of real data. We conclude that although the synthetic samples resemble the real ones, the classification improvement when simply augmenting the real dataset is barely noticeable on individual datasets, but when both datasets are merged the results show an increase across all metrics for the classifiers when using synthetic samples as augmented data. From the fine-tuning results the Time-VQVAE generative model has shown to be superior to the others but not powerful enough to achieve results close to a classifier trained with real data only. In addition, methods and metrics for measuring closeness between synthetic data and the real one have been explored as a side effect of the main research questions of this study.

翻译：深度学习模型需要足够的数据量才能发现其中隐藏的模式。生成式建模的目的正是学习数据分布，从而允许我们采样更多数据以扩充原始数据集。在生理数据（更具体地说是心电图数据）的背景下，考虑到其敏感性和昂贵的数据收集成本，我们可以利用生成模型的优势来扩展现有数据集并改进下游任务——在本研究中即心律分类。本工作中，我们探索了使用深度学习中的不同生成模型（即Diffweave、Time-Diffusion和Time-VQVAE）生成的合成数据对两个开源多变量心电图数据集分类效果的提升作用。此外，我们还研究了迁移学习的效果：首先对合成数据预训练的模型进行微调，然后逐步增加真实数据的比例。我们的结论是：虽然合成样本与真实样本相似，但仅通过简单扩充真实数据集带来的分类改进在单个数据集上几乎不可察觉；而当合并两个数据集时，使用合成样本作为增强数据能使所有分类器的各项指标均得到提升。从微调结果来看，Time-VQVAE生成模型表现优于其他模型，但仍不足以达到仅用真实数据训练的分类器的性能水平。此外，作为本研究主要研究问题的衍生成果，我们还探索了衡量合成数据与真实数据接近程度的方法与指标。