mlsanityData to 7 Oct 2026

Text-to-ECG: 12-Lead Electrocardiogram Synthesis conditioned on Clinical Text Reports

arXiv 2303.09395 ↗First posted 9 Mar 2023cs.CL cs.LG eess.SPcited by 13

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Abstract (arXiv)

Electrocardiogram (ECG) synthesis is the area of research focused on generating realistic synthetic ECG signals for medical use without concerns over annotation costs or clinical data privacy restrictions. Traditional ECG generation models consider a single ECG lead and utilize GAN-based generative models. These models can only generate single lead samples and require separate training for each diagnosis class. The diagnosis classes of ECGs are insufficient to capture the intricate differences between ECGs depending on various features (e.g. patient demographic details, co-existing diagnosis classes, etc.). To alleviate these challenges, we present a text-to-ECG task, in which textual inputs are used to produce ECG outputs. Then we propose Auto-TTE, an autoregressive generative model conditioned on clinical text reports to synthesize 12-lead ECGs, for the first time to our knowledge. We compare the performance of our model with other representative models in text-to-speech and text-to-image. Experimental results show the superiority of our model in various quantitative evaluations and qualitative analysis. Finally, we conduct a user study with three board-certified cardiologists to confirm the fidelity and semantic alignment of generated samples. our code will be available at https://github.com/TClife/text_to_ecg

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 22 of its 29 references that are in the map. First posted on arXiv 9 Mar 2023.

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22 of 29 references.

References · 22

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10 more
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Citing papers per year: 2023: 1; 2024: 3; 2025: 7; 2026: 2.

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  1. 2023TEST: Text Prototype Aligned Embedding to Activate LLM's Ability for Time Seriescited by 165
  2. 2024Large Language Models for Time Series: A Surveycited by 113
  3. 2025How Can Time Series Analysis Benefit From Multiple Modalities? A Survey and Outlookcited by 20
  4. 2025DiffuSETS: 12-lead ECG Generation Conditioned on Clinical Text Reports and Patient-Specific Informationcited by 15
  5. 2024Personalized Heart Disease Detection via ECG Digital Twin Generationcited by 7
  6. 2025Nature's Insight: A Novel Framework and Comprehensive Analysis of Agentic Reasoning Through the Lens of Neurosciencecited by 3
  7. 2024LiPCoT: Linear Predictive Coding based Tokenizer for Self-supervised Learning of Time Series Data via Language Modelscited by 2
  8. 2025A Time Series Multitask Framework Integrating a Large Language Model, Pre-Trained Time Series Model, and Knowledge Graphcited by 1
  9. 2025ECGTwin: Personalized ECG Generation Using Controllable Diffusion Modelcited by 1
  10. 2026Prompting Underestimates LLM Capability for Time Series Classificationcited by 1
  11. 2025MM-DADM: Multimodal Drug-Aware Diffusion Model for Virtual Clinical Trialscited by 0
  12. 2025Challenges and Limitations of Generative AI in Synthesizing Wearable Sensor Datacited by 0
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  1. 2026Large language models in medical time series analysiscited by 0