Simulator and Experience Enhanced Diffusion Model for Comprehensive ECG Generation
Xiaoda Wang, Kaiqiao Han, Yuhao Xu, Xiao Luo, Yizhou Sun, Wei Wang, Carl Yang

TL;DR
SE-Diff is a novel ECG generation framework that combines physiological simulation and clinical experience to produce realistic, physiologically plausible ECG signals, improving data quality and aiding downstream analysis.
Contribution
It introduces a new diffusion model that integrates a physiological ECG simulator and clinical experience knowledge for comprehensive ECG generation.
Findings
Enhanced signal fidelity over baseline models
Improved semantic alignment in text-to-ECG generation
Beneficial for downstream ECG classification tasks
Abstract
Cardiovascular disease (CVD) is a leading cause of mortality worldwide. Electrocardiograms (ECGs) are the most widely used non-invasive tool for cardiac assessment, yet large, well-annotated ECG corpora are scarce due to cost, privacy, and workflow constraints. Generating ECGs can be beneficial for the mechanistic understanding of cardiac electrical activity, enable the construction of large, heterogeneous, and unbiased datasets, and facilitate privacy-preserving data sharing. Generating realistic ECG signals from clinical context is important yet underexplored. Recent work has leveraged diffusion models for text-to-ECG generation, but two challenges remain: (i) existing methods often overlook the physiological simulator knowledge of cardiac activity; and (ii) they ignore broader, experience-based clinical knowledge grounded in real-world practice. To address these gaps, we propose…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsECG Monitoring and Analysis · Cardiac electrophysiology and arrhythmias · Machine Learning in Healthcare
