Abstract
The widespread emergence of smart devices for ECG has sparked demand for intelligent single-lead ECG-based diagnostic systems. However, it is challenging to develop a single-lead-based ECG interpretation model for multiple diseases diagnosis due to the lack of some key disease information. In this work, we propose inter-lead Multi-View Knowledge Transferring of ECG (MVKT-ECG) to boost single-lead ECG's ability for multi-label disease diagnosis. This training strategy can transfer superior disease knowledge from multiple different views of ECG (e.g. 12-lead ECG) to single-lead-based ECG interpretation model to mine details in single-lead ECG signals that are easily overlooked by neural networks. MVKT-ECG allows this lead variety as a supervision signal within a teacher-student paradigm, where the teacher observes multi-lead ECG educates a student who observes only single-lead ECG. Since the mutual disease information between the single-lead ECG and muli-lead ECG plays a key role in knowledge transferring, we present a new disease-aware Contrastive Lead-information Transferring(CLT) to improve the mutual disease information between the single-lead ECG and muli-lead ECG. Moreover, We modify traditional Knowledge Distillation to multi-label disease Knowledge Distillation (MKD) to make it applicable for multi-label disease diagnosis. The comprehensive experiments verify that MVKT-ECG has an excellent performance in improving the diagnostic effect of single-lead ECG.
Abstract (translated)
广泛使用智能设备进行心电图(ECG)检测引起了对单电极ECG诊断系统的需求。然而,由于缺乏某些关键疾病信息,开发单电极ECG用于多标签疾病诊断的挑战性很大。在本文中,我们提出了跨电极ECG知识的多视角知识传输(MVKT-ECG)方案,以增强单电极ECG的多标签疾病诊断能力。该训练策略可以将高级疾病知识从多个视角的ECG(如12电极ECG)转移到单电极ECG解释模型,以挖掘由神经网络容易忽略的单电极ECG信号中的详细信息。MVKT-ECG将这种电极多样性作为监督信号,在教师和学生范式中,让教师观察多电极ECG并教育仅观察单电极ECG的学生。由于单电极ECG和多电极ECG之间的相互疾病信息在知识传输中发挥着关键作用,我们提出了一种新的疾病awareContrastive电极信息传输(CLT)方法,以改善单电极ECG和多电极ECG之间的相互疾病信息。此外,我们对传统知识蒸馏进行了修改,将其转化为多标签疾病知识蒸馏(MKD),使其适用于多标签疾病诊断。全面实验证实,MVKT-ECG在改善单电极ECG诊断效果方面表现良好。
URL
https://arxiv.org/abs/2301.12178
