Heart diseases rank among the leading causes of global mortality,
demonstrating a crucial need for early diagnosis and intervention. Most
traditional electrocardiogram (ECG) based automated diagnosis methods are
trained at population level, neglecting the customization of personalized ECGs
to enhance individual healthcare management. A potential solution to address
this limitation is to employ digital twins to simulate symptoms of diseases in
real patients. In this paper, we present an innovative prospective learning
approach for personalized heart disease detection, which generates digital
twins of healthy individuals' anomalous ECGs and enhances the model sensitivity
to the personalized symptoms. In our approach, a vector quantized feature
separator is proposed to locate and isolate the disease symptom and normal
segments in ECG signals with ECG report guidance. Thus, the ECG digital twins
can simulate specific heart diseases used to train a personalized heart disease
detection model. Experiments demonstrate that our approach not only excels in
generating high-fidelity ECG signals but also improves personalized heart
disease detection. Moreover, our approach ensures robust privacy protection,
safeguarding patient data in model development.

基于数字孪生技术的个性化心脏病检测模型可以生成高质量的心电图信号，并改善个性化心脏病检测，在保证隐私安全的前提下有效管理患者数据。

通过 ECG 数字孪生生成个性化心脏病检测

Personalized Heart Disease Detection via ECG Digital Twin Generation

Remote patient monitoring based on wearable single-lead electrocardiogram
(ECG) devices has significant potential for enabling the early detection of
heart disease, especially in combination with artificial intelligence (AI)
approaches for automated heart disease detection. There have been prior studies
applying AI approaches based on deep learning for heart disease detection.
However, these models are yet to be widely accepted as a reliable aid for
clinical diagnostics, in part due to the current black-box perception
surrounding many AI algorithms. In particular, there is a need to identify the
key features of the ECG signal that contribute toward making an accurate
diagnosis, thereby enhancing the interpretability of the model. In the present
study, we develop a vision transformer approach to identify atrial fibrillation
based on single-lead ECG data. A residual network (ResNet) approach is also
developed for comparison with the vision transformer approach. These models are
applied to the Chapman-Shaoxing dataset to classify atrial fibrillation, as
well as another common arrhythmia, sinus bradycardia, and normal sinus rhythm
heartbeats. The models enable the identification of the key regions of the
heartbeat that determine the resulting classification, and highlight the
importance of P-waves and T-waves, as well as heartbeat duration and signal
amplitude, in distinguishing normal sinus rhythm from atrial fibrillation and
sinus bradycardia.

发展了一种基于远程患者监测的穿戴式单导联心电图（ECG）设备的视觉变换方法，可以识别房颤并鉴别正常窦律和窦性心动过缓。

揭示心跳特征：采用视觉转换器方法解释心电图信号中的可解释性房颤检测

Deciphering Heartbeat Signatures: A Vision Transformer Approach to  Explainable Atrial Fibrillation Detection from ECG Signals

In recent years, deep generative models have gained attention as a promising
data augmentation solution for heart disease detection using deep learning
approaches applied to ECG signals. In this paper, we introduce a novel approach
based on denoising diffusion probabilistic models for ECG synthesis that covers
three scenarios: heartbeat generation, partial signal completion, and full
heartbeat forecasting. Our approach represents the first generalized
conditional approach for ECG synthesis, and our experimental results
demonstrate its effectiveness for various ECG-related tasks. Moreover, we show
that our approach outperforms other state-of-the-art ECG generative models and
can enhance the performance of state-of-the-art classifiers.

本文提出了一种基于去噪扩散概率模型的心电波形合成方法，此方法可以生成心跳、完成部分信号或进行心跳预测，且其效果较其他同类方法更佳，为心电信号合成提供了一个更为广义的条件方法。

DiffECG: 一种推广的基于概率的扩散模型用于合成心电图信号

DiffECG: A Generalized Probabilistic Diffusion Model for ECG Signals  Synthesis

In this paper, we focus on a new method of data augmentation to solve the
data imbalance problem within imbalanced ECG datasets to improve the robustness
and accuracy of heart disease detection. By using Optimal Transport, we augment
the ECG disease data from normal ECG beats to balance the data among different
categories. We build a Multi-Feature Transformer (MF-Transformer) as our
classification model, where different features are extracted from both time and
frequency domains to diagnose various heart conditions. Learning from 12-lead
ECG signals, our model is able to distinguish five categories of cardiac
conditions. Our results demonstrate 1) the classification models' ability to
make competitive predictions on five ECG categories; 2) improvements in
accuracy and robustness reflecting the effectiveness of our data augmentation
method.

本文通过使用最优传输方法对 ECG 疾病数据进行增广以平衡不同类别之间的数据来解决不平衡 ECG 数据集中的数据不平衡问题，从而提高心脏病检测的鲁棒性和准确性，构建了一个从时间和频率域提取不同特征来诊断各种心脏病的多特征变压器（MF-Transformer）分类模型，能够从 12 导联 ECG 信号中学习，识别 5 个心脏病类别，并通过实验展示了该方法的有效性。