@article{Xia2025, 
author = {Pan Xia and Zhongrui Bai and Yicheng Yao and Lirui Xu and Hao Zhang and Lidong Du and Xianxiang Chen and Qiao Ye and Yusi Zhu and Peng Wang and Xiaoran Li and Guangyun Wang and Zhen Fang},
title = {Advanced Deep Neural Network with Unified Feature-Aware and Label Embedding for Multi-Label Arrhythmias Classification},
year = {2025},
journal = {Tsinghua Science and Technology},
volume = {30},
number = {3},
pages = {1251-1269},
keywords = {deep neural network, electrocardiogram, label embedding, multi-label arrhythmias classification},
url = {https://www.sciopen.com/article/10.26599/TST.2023.9010162},
doi = {10.26599/TST.2023.9010162},
abstract = {Multi-label arrhythmias classification is of great significance to the diagnosis of cardiovascular disease, and it is a challenging task as it requires identifying the label subset most related to each instance. In this paper, by integrating a deep residual neural network and auto-encoder, we propose an advanced deep neural network (DNN) framework with unified feature-aware and label embedding to perform multi-label arrhythmias classification involving 30 types of arrhythmias. Firstly, a deep residual neural network is built to extract the complex pathological features from varying-dimensional electrocardiograms (ECGs). Secondly, the mean square error loss is adopted to learn a latent space associating the deep pathological features and the corresponding label data, and then to achieve unified feature-label embedding. Thirdly, the label-correlation aware loss is introduced to optimize the auto-encoder architecture, which enables our model to exploit label-correlation for improved multi-label prediction. Our proposed DNN model can allow end-to-end training and prediction, which can perform feature-aware, label embedding, and label-correlation aware prediction in a unified framework. Finally, our proposed model is evaluated on the currently largest public dataset worldwide, and achieves the challenge metric scores of 0.492, 0.495, and 0.490 on the 12-lead, 3-lead, and all-lead version ECGs, respectively. The performance of our approach outperforms other current state-of-the-art methods in the leave-one-dataset-out cross-validation setting, which demonstrates that our approach has great competitiveness in identifying a wider range of multi-label arrhythmias.}
}