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 this https URL

本文提出了基于临床文字报告的自回归生成模型 Auto-TTE，用于合成 12 导联心电图的文本转 ECG 任务，并与其他代表模型进行比较，实验结果表明我们的模型在各种定量评估和定性分析中具有优越性，经过三名美国心脏病学会认证心脏病专家的用户研究，证实了生成样本的保真度和语义对齐度。