期刊
COMPUTERS IN BIOLOGY AND MEDICINE
卷 166, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compbiomed.2023.107503
关键词
Single-lead Electrocardiogram; Arrhythmia classification; Deep learning; Knowledge transfer; Neural network
The aim of this study is to improve the diagnostic capabilities of single-lead ECG for multi-label disease classification by transferring disease knowledge from multi-lead ECG to a single-lead ECG interpretation model using a teacher-student approach. The study presents a new method called Contrastive Lead-information Transferring (CLT) and modifies Knowledge Distillation into Multi-label disease Knowledge Distillation (MKD) to facilitate the transfer of disease information between different views of ECG. The experiments demonstrate significant improvements in diagnostic performance for single-lead ECG.
Electrocardiogram (ECG) is a widely used technique for diagnosing cardiovascular disease. The widespread emergence of smart ECG devices has sparked the demand for intelligent single-lead ECG-based diagnostic systems. However, it is challenging to develop a single-lead-based ECG interpretation model for multiple disease diagnosis due to the lack of some key disease information. We aim to improve the diagnostic capabilities of single-lead ECG for multi-label disease classification in a new teacher-student manner, where the teacher trained by multi-lead ECG educates a student who observes only single-lead ECG We present a new disease aware Contrastive Lead-information Transferring (CLT) to improve the mutual disease information between the single-lead-based ECG interpretation model and multi-lead-based ECG interpretation model. Moreover, We modify the traditional Knowledge Distillation into Multi-label disease Knowledge Distillation (MKD) to make it applicable for multi-label disease diagnosis. The whole knowledge transferring process is inter-lead Multi -View Knowledge Transferring of ECG (MVKT-ECG). By employing the training strategy, we can effectively transfer comprehensive disease knowledge from various views of ECG, such as the 12-lead ECG, to a single-lead-based ECG interpretation model. This enables the model to extract intricate details from single-lead ECG signals and enhances the model's capability of diagnosing and identifying single-lead signals. Extensive experiments on two commonly used public multi-label datasets, ICBEB2018 and PTB-XL demonstrate that our MVKT-ECG yields exceptional diagnostic performance improvements for single-lead ECG. The student outperforms its baseline observably on the PTB-XL dataset (1.3 % on PTB.super, and 1.4 % on PTB.sub), and on ICBEB2018 dataset (3.2 %).
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