DiffuSETS: 12-Lead ECG generation conditioned on clinical text reports and patient-specific information.

ABSTRACT: The scarcity of high-quality electrocardiogram (ECG) data, driven by privacy concerns and limited medical resources, creates a pressing need for effective ECG signal generation. Existing approaches for generating ECG signals typically rely on small training datasets, lack comprehensive evaluation frameworks, and overlook potential applications beyond data augmentation. To address these challenges, we propose DiffuSETS, a framework capable of generating ECG signals with high semantic alignment and fidelity. DiffuSETS accepts various modalities of clinical text reports and patient-specific information as inputs, enabling the creation of clinically meaningful ECG signals. Additionally, we introduce a comprehensive benchmarking methodology to assess the effectiveness of ECG generative models.

SUBMITTER: Lai Y 

PROVIDER: S-EPMC12546759 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature