Text-to-ECG: 12-Lead Electrocardiogram Synthesis conditioned on Clinical Text Reports
This work addresses the need for synthetic ECG data in medical applications to reduce annotation costs and privacy concerns, offering a novel approach that generates multi-lead ECGs conditioned on detailed clinical text, which is an incremental advancement over single-lead methods.
The paper tackles the problem of generating realistic 12-lead electrocardiogram (ECG) signals by introducing a text-to-ECG task, where clinical text reports are used as input to synthesize ECGs, and proposes Auto-TTE, an autoregressive model that outperforms other models in quantitative evaluations and qualitative analysis, with user studies by cardiologists confirming fidelity and alignment.
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