  In this paper, we propose a novel graph-based data augmentation method that
can generally be applied to medical waveform data with graph structures. In the
process of recording medical waveform data, such as electrocardiogram (ECG) or
electroencephalogram (EEG), angular perturbations between the measurement leads
exist due to discrepancies in lead positions. The data samples with large
angular perturbations often cause inaccuracy in algorithmic prediction tasks.
We design a graph-based data augmentation technique that exploits the inherent
graph structures within the medical waveform data to improve both performance
and robustness. In addition, we show that the performance gain from graph
augmentation results from robustness by testing against adversarial attacks.
Since the bases of performance gain are orthogonal, the graph augmentation can
be used in conjunction with existing data augmentation techniques to further
improve the final performance. We believe that our graph augmentation method
opens up new possibilities to explore in data augmentation.
