Cardiac Disease Diagnosis on Imbalanced Electrocardiography Data Through Optimal Transport Augmentation
Jielin Qiu, Jiacheng Zhu, Mengdi Xu, Peide Huang, Michael A. Rosenberg, Douglas Weber, Emerson Liu, Ding Zhao
Abstract
In this paper, we focus on a new method of data augmentation to solve the data imbalance problem within imbalanced ECG datasets to improve the robustness and accuracy of heart disease detection. By using Optimal Transport, we augment the ECG disease data from normal ECG beats to balance the data among different categories. We build a Multi-Feature Transformer (MF-Transformer) as our classification model, where different features are extracted from both time and frequency domains to diagnose various heart conditions. Our results demonstrate 1) the classification models’ ability to make competitive predictions on five ECG categories; 2) improvements in accuracy and robustness reflecting the effectiveness of our data augmentation method.
BibTeX
@inproceedings{icassp2023_cardiacdiseasedi,
title = {Cardiac Disease Diagnosis on Imbalanced Electrocardiography Data Through Optimal Transport Augmentation},
author = {Jielin Qiu and Jiacheng Zhu and Mengdi Xu and Peide Huang and Michael A. Rosenberg and Douglas Weber and Emerson Liu and Ding Zhao},
booktitle = {ICASSP 2023},
year = {2023}
}