DiffuSETS: 12-lead ECG Generation Conditioned on Clinical Text Reports and Patient-Specific Information
This work addresses data scarcity for medical researchers and clinicians in cardiology, though it appears incremental as it builds on existing generative approaches with new conditioning inputs.
The paper tackles the problem of generating realistic 12-lead ECG signals to address data scarcity in cardiology by proposing DiffuSETS, a framework that uses clinical text reports and patient-specific information as inputs to create clinically meaningful ECG signals with high semantic alignment and fidelity.
Heart disease remains a significant threat to human health. As a non-invasive diagnostic tool, the electrocardiogram (ECG) is one of the most widely used methods for cardiac screening. However, the scarcity of high-quality 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 novel 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, to address the lack of standardized evaluation in ECG generation, we introduce a comprehensive benchmarking methodology to assess the effectiveness of generative models in this domain. Our model achieve excellent results in tests, proving its superiority in the task of ECG generation. Furthermore, we showcase its potential to mitigate data scarcity while exploring novel applications in cardiology education and medical knowledge discovery, highlighting the broader impact of our work.