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In this paper, we focus on a new method of data augmentation to solve thedata imbalance problem within imbalanced ECG datasets to improve the robustnessand accuracy of heart disease detection. By using Optimal Transport, we augmentthe ECG disease data from normal ECG beats to balance the data among differentcategories. We build a Multi-Feature Transformer (MF-Transformer) as ourclassification model, where different features are extracted from both time andfrequency domains to diagnose various heart conditions. Learning from 12-leadECG signals, our model is able to distinguish five categories of cardiacconditions. Our results demonstrate 1) the classification models' ability tomake competitive predictions on five ECG categories; 2) improvements inaccuracy and robustness reflecting the effectiveness of our data augmentationmethod.

Cardiac Disease Diagnosis on Imbalanced Electrocardiography Data Through Optimal Transport Augmentation