MVKT-ECG: Efficient single-lead ECG classification for multi-label arrhythmia by multi-view knowledge transferring

Yuzhen Qin; Li Sun; Hui Chen; Wenming Yang; Wei-Qiang Zhang; Jintao Fei; Guijin Wang

doi:10.1016/j.compbiomed.2023.107503

MVKT-ECG: Efficient single-lead ECG classification for multi-label arrhythmia by multi-view knowledge transferring

Comput Biol Med. 2023 Sep 19:166:107503. doi: 10.1016/j.compbiomed.2023.107503. Online ahead of print.

Authors

Yuzhen Qin¹, Li Sun², Hui Chen², Wenming Yang¹, Wei-Qiang Zhang², Jintao Fei³, Guijin Wang⁴

Affiliations

¹ Shenzhen International Graduate School, Tsinghua University, Shenzhen 518071, China.
² Department of Electronic Engineering, Tsinghua University, Beijing 100084, China.
³ Beijing Tsinghua Changgung Hospital, Beijing 102218, China.
⁴ Department of Electronic Engineering, Tsinghua University, Beijing 100084, China. Electronic address: wangguijin@tsinghua.edu.cn.

PMID: 37806055
DOI: 10.1016/j.compbiomed.2023.107503

Abstract

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 %).

Keywords: Arrhythmia classification; Deep learning; Knowledge transfer; Neural network; Single-lead Electrocardiogram.