mlsanityData to 14 Oct 2026

Personalized Heart Disease Detection via ECG Digital Twin Generation

arXiv 2404.11171 ↗First posted 17 Apr 2024cs.LG cs.AI eess.SPcited by 7

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Abstract (arXiv)

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.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 24 of its 36 references that are in the map. First posted on arXiv 17 Apr 2024.

Builds on

24 of 36 references.

References · 24

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 5; 2026: 1.

7 papers.

  1. 2025DiffuSETS: 12-lead ECG Generation Conditioned on Clinical Text Reports and Patient-Specific Informationcited by 16
  2. 2024MambaCapsule: Towards Transparent Cardiac Disease Diagnosis with Electrocardiography Using Mamba Capsule Networkcited by 5
  3. 2025ECGTwin: Personalized ECG Generation Using Controllable Diffusion Modelcited by 2
  4. 2026Signal or Noise? Understanding Generative Models for Real-World Sensor Time Seriescited by 2
  5. 2025MM-DADM: Multimodal Drug-Aware Diffusion Model for Virtual Clinical Trialscited by 0
  6. 2025Application of Contrastive Learning on ECG Data: Evaluating Performance in Japanese and Classification with Around 100 Labelscited by 0
  7. 2025Versatile and Risk-Sensitive Cardiac Diagnosis via Graph-Based ECG Signal Representationcited by 0

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  1. 2026Privacy-Preserving Federated Autoencoder for ECG Anomaly Detection on Edge Devices
  2. 2025Interpretable Multimodal Zero-Shot ECG Diagnosis via Structured Clinical Knowledge Alignment
  3. 2023DiffECG: A Versatile Probabilistic Diffusion Model for ECG Signals Synthesis
  4. 2025High-Fidelity Synthetic ECG Generation via Mel-Spectrogram Informed Diffusion Training
  5. 2023MVKT-ECG: Efficient Single-lead ECG Classification on Multi-Label Arrhythmia by Multi-View Knowledge Transferring
  6. 2024SSSD-ECG-nle: New Label Embeddings with Structured State-Space Models for ECG generation
  7. 2023Towards quantitative precision for ECG analysis: Leveraging state space models, self-supervision and patient metadata
  8. 2025UniECG: Understanding and Generating ECG in One Unified Model