Spectral Optimal Transport for Electrocardiogram Data Augmentation

Abstract

Automatic diagnosis has been extensively studied in the medical field. In particular, the classification problem is one of the typical approaches. The application of supervised machine learning to heart disease detection using electrocardiogram (ECG) data has also been studied extensively in recent years, with neural-network-based models recording high accuracy. However, annotation cost and label imbalance are often challenges in these studies. Espe- cially in the medical domain, labeling requires expertise, and positive examples are often extremely rare compared to negative examples, making it difficult to prepare high-quality data on a large scale. Data augmentation methods can be effective in addressing these issues. Data augmentation is the process of creating artificial data by performing certain operations, such as perturbation, on the original data/label pairs. In this study, data augmentation is used to improve the performance in detecting low left ventricular ejection fraction (LVEF). Although data augmentation has been popular in the field of image processing, it is still in its developing state for time series data. In this paper, we report on the effectiveness of a method that combines multiple data using optimal transport in the frequency domain of the ECG to obtain augmented samples.