Cardiac Disease Diagnosis on Imbalanced Electrocardiography Data Through Optimal Transport Augmentation
- Resource Type
- Conference
- Authors
- Qiu, Jielin; Zhu, Jiacheng; Xu, Mengdi; Huang, Peide; Rosenberg, Michael; Weber, Douglas; Liu, Emerson; Zhao, Ding
- Source
- ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Acoustics, Speech and Signal Processing (ICASSP), ICASSP 2023 - 2023 IEEE International Conference on. :1-5 Jun, 2023
- Subject
- Bioengineering
Communication, Networking and Broadcast Technologies
Computing and Processing
Signal Processing and Analysis
Heart
Frequency-domain analysis
Electrocardiography
Signal processing
Predictive models
Feature extraction
Transformers
Data augmentation
ECG
Optimal Transport
Transformer
Imbalance
- Language
- ISSN
- 2379-190X
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.