  The adoption of deep learning in ECG diagnosis is often hindered by the
scarcity of large, well-labeled datasets in real-world scenarios, leading to
the use of transfer learning to leverage features learned from larger datasets.
Yet the prevailing assumption that transfer learning consistently outperforms
training from scratch has never been systematically validated. In this study,
we conduct the first extensive empirical study on the effectiveness of transfer
learning in multi-label ECG classification, by investigating comparing the
fine-tuning performance with that of training from scratch, covering a variety
of ECG datasets and deep neural networks. We confirm that fine-tuning is the
preferable choice for small downstream datasets; however, when the dataset is
sufficiently large, training from scratch can achieve comparable performance,
albeit requiring a longer training time to catch up. Furthermore, we find that
transfer learning exhibits better compatibility with convolutional neural
networks than with recurrent neural networks, which are the two most prevalent
architectures for time-series ECG applications. Our results underscore the
importance of transfer learning in ECG diagnosis, yet depending on the amount
of available data, researchers may opt not to use it, considering the
non-negligible cost associated with pre-training.
