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Open Access

Advanced Deep Neural Network with Unified Feature-Aware and Label Embedding for Multi-Label Arrhythmias Classification

Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China, and also with School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
Research Unit of Personalized Management of Chronic Respiratory Disease, Chinese Academy of Medical Sciences, Beijing 100190, China
Air Force Medical Center, Air Force Medical University, Beijing 100142, China
School of Physics and Electronic Information, Yunnan Normal University, Kunming 650500, China
Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China
Show Author Information

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.

Keywords

electrocardiogrammulti-label arrhythmias classificationdeep neural networklabel embedding

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Tsinghua Science and Technology
Volume 30 Issue 3,
June 2025
Pages 1251-1269
DOI: 10.26599/TST.2023.9010162
Cite this article:
Xia P, Bai Z, Yao Y, et al. Advanced Deep Neural Network with Unified Feature-Aware and Label Embedding for Multi-Label Arrhythmias Classification. Tsinghua Science and Technology, 2025, 30(3): 1251-1269. https://doi.org/10.26599/TST.2023.9010162

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Received: 19 November 2023
Revised: 17 December 2023
Accepted: 24 December 2023
Published: 30 December 2024
© The Author(s) 2025.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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