Optimal Transport based Data Augmentation for Heart Disease Diagnosis and Prediction

• URL: http://arxiv.org/abs/2202.00567v1
• Date: Tue, 25 Jan 2022 03:49:28 GMT
• Title: Optimal Transport based Data Augmentation for Heart Disease Diagnosis and Prediction
• Authors: Jielin Qiu, Jiacheng Zhu, Michael Rosenberg, Emerson Liu, Ding Zhao
• Abstract summary: We propose a new method of data augmentation to solve the data imbalance problem within imbalanced ECG datasets. By using Optimal Transport, we augment the ECG disease data from normal ECG beats to balance the data among different categories. 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.
• Score: 16.7288675686184
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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. Learning from 12-lead ECG signals, our model is able to distinguish five categories of cardiac 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.

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