Text-to-ECG: a Framework to Generate 12-Lead ECG from Text Reports

Synthetic electrocardiograms (ECGs), obtained with Generative Artificial Intelligence (GenAI), are currently used to support the training of other AI algorithms, most often decision support systems, by augmenting the dataset for the minority classes. In this study, we proposed a Text-to-ECG (T2ECG) framework, which could generate synthetic ECG beats from textual data. The framework made use of two components. The first, Bio\_ClinicalBERT, produced an embedded vector from the input text (e.g., ``left ventricular hypertrophy''). Then, a second component leveraged such representation to generate a 12-lead ECG heartbeat by means of a Wasserstein Generative Adversarial Network with gradient penalty. The training was performed on the PTB-XL dataset, freely available on Physionet. The framework was designed to generate five different diagnostic classes: i) normal sinus rhythm; ii) inferior myocardial infarction (IMI); iii) antero-septal myocardial infarction (ASMI); iv) left anterior fascicular block (LAFB); and v) left ventricular hypertrophy (LVH). The realism of the generated signals was assessed through three different methodologies, involving both visual inspection and quantitative analyses. Our results show that the T2ECG framework was able to generate heartbeats of sufficient quality, except for the the ASMI class. In conclusion, the framework proposed does not only support data augmentation but also facilitates the creation of ECGs by non-technical users, offering a textual interface to the GenAI model.