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.

提出了一种新的单导联心电信号的多标记疾病诊断方法，即基于多视角知识转移，利用其中的疾病信息在师生框架下实现单导联心电信号的诊断，实验证实该方法有出色的性能。

MVKT-ECG：多视角知识转移下的多标签心律失常高效单导联分类

MVKT-ECG: Efficient Single-lead ECG Classification on Multi-Label Arrhythmia by Multi-View Knowledge Transferring

Sleep apnea (SA) is a type of sleep disorder characterized by snoring and
chronic sleeplessness, which can lead to serious conditions such as high blood
pressure, heart failure, and cardiomyopathy (enlargement of the muscle tissue
of the heart). The electrocardiogram (ECG) plays a critical role in identifying
SA since it might reveal abnormal cardiac activity. Recent research on
ECG-based SA detection has focused on feature engineering techniques that
extract specific characteristics from multiple-lead ECG signals and use them as
classification model inputs. In this study, a novel method of feature
extraction based on the detection of S peaks is proposed to enhance the
detection of adjacent SA segments using a single-lead ECG. In particular, ECG
features collected from a single lead (V2) are used to identify SA episodes. On
the extracted features, a CNN model is trained to detect SA. Experimental
results demonstrate that the proposed method detects SA from single-lead ECG
data is more accurate than existing state-of-the-art methods, with 91.13%
classification accuracy, 92.58% sensitivity, and 88.75% specificity. Moreover,
the further usage of features associated with the S peaks enhances the
classification accuracy by 0.85%. Our findings indicate that the proposed
machine learning system has the potential to be an effective method for
detecting SA episodes.

提出了一种基于 S 峰检测的特征提取方法，使用单导联 ECG 来增强睡眠呼吸暂停的检测，并使用卷积神经网络进行训练，实现了比现有方法更高的准确性和敏感度，并表明该机器学习系统有望成为检测睡眠呼吸暂停的有效方法。