The scarcity and imbalance of datasets for training deep learning models in a specific task is a common problem. This is especially true in the physiological domain where many applications use complex data collection processes and protocols, and it is difficult to gather a significant number of subjects.
In this paper, we evaluate generative deep learning algorithms by training them to create data based on open physiological datasets and conduct a study on their potential for transfer learning. We measure the performance change of classifiers when the training data is augmented with the synthetic samples and also perform experiments in which we fine-tune classification models trained with the generated data adding increasing amounts of the real data to investigate the transfer learning capabilities of synthetic datasets.
Finally, we advise and provide the best option for researchers interested in augmenting ECG datasets using these algorithms and the best fine-tuning strategies that would generalize correctly when tested on new data from the same domain but for a different classification task.
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