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Text-to-ECG: a Framework to Generate 12-Lead ECG from Text Reports
Summary
A new Text-to-ECG (T2ECG) framework generates synthetic electrocardiogram (ECG) beats from text descriptions using Generative AI. This approach aids AI training data augmentation and simplifies ECG creation for non-experts.
Area of Science:
- Artificial Intelligence in Medicine
- Biomedical Signal Processing
- Machine Learning for Healthcare
Background:
- Generative Artificial Intelligence (GenAI) is used to create synthetic electrocardiograms (ECGs) for augmenting training datasets, particularly for minority classes in AI decision support systems.
- Existing methods often require specialized expertise for synthetic ECG generation.
Purpose of the Study:
- To introduce a novel Text-to-ECG (T2ECG) framework capable of generating synthetic 12-lead ECG heartbeats directly from textual diagnostic descriptions.
- To evaluate the quality and realism of ECGs generated by the T2ECG framework for various cardiac conditions.
Main Methods:
- The T2ECG framework utilizes Bio_ClinicalBERT to create text embeddings and a Wasserstein Generative Adversarial Network with gradient penalty to synthesize ECG beats.
- Training was conducted on the PTB-XL dataset, focusing on five diagnostic classes: normal sinus rhythm, inferior myocardial infarction (IMI), antero-septal myocardial infarction (ASMI), left anterior fascicular block (LAFB), and left ventricular hypertrophy (LVH).
- Signal realism was assessed using visual inspection and quantitative analyses.
Main Results:
- The T2ECG framework successfully generated high-quality synthetic ECG heartbeats for most diagnostic classes.
- The generated signals for antero-septal myocardial infarction (ASMI) were found to be of insufficient quality.
- The framework demonstrated potential for data augmentation and simplified ECG generation via a text-based interface.
Conclusions:
- The T2ECG framework offers a viable method for generating synthetic ECGs from text, supporting AI model training and potentially democratizing ECG data creation.
- While effective for several conditions, further refinement is needed for specific classes like ASMI.
- This text-to-signal approach streamlines the process, making AI-driven ECG generation more accessible.
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