# Graph structure based data augmentation method

## TL;DR

This paper introduces a new data augmentation method for medical waveform data using graph structures, improving prediction accuracy and model robustness.

## Contribution

A novel graph-based data augmentation method is proposed, improving F1 scores and robustness in medical waveform data tasks.

## Key findings

- Graph Augmentation improves F1 score by 1.44% across various tasks, models, and datasets.
- The method enhances model robustness against adversarial attacks.
- Combining Graph Augmentation with existing techniques boosts F1 score by an additional 2.47%.

## Abstract

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), angular perturbations between the measurement leads exist due to imperfections 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 the F1 score by 1.44% over various tasks, models, and datasets. In addition, we show that Graph Augmentation improves model robustness by testing against adversarial attacks. Since Graph Augmentation is methodologically orthogonal to existing data augmentation techniques, they can be used in conjunction to further improve the final performance, resulting in a 2.47% gain of the F1 score. We believe that our Graph Augmentation method opens up new possibilities to explore in data augmentation.