5 papers
ECG-MoE: Mixture-of-Expert Electrocardiogram Foundation Model
Yuhao Xu, Xiaoda Wang, Yi Wu +3
Electrocardiography (ECG) analysis is crucial for cardiac diagnosis, yet existing foundation models often fail to capture the periodicity and diverse features required for varied c…
EnECG: Efficient Ensemble Learning for Electrocardiogram Multi-task Foundation Model
Yuhao Xu, Xiaoda Wang, Jiaying Lu +6
Electrocardiogram (ECG) analysis plays a vital role in the early detection, monitoring, and management of various cardiovascular conditions. While existing models have achieved not…
An Electrocardiogram Multi-task Benchmark with Comprehensive Evaluations and Insightful Findings
Yuhao Xu, Jiaying Lu, Sirui Ding +3
In the process of patient diagnosis, non-invasive measurements are widely used due to their low risks and quick results. Electrocardiogram (ECG), as a non-invasive method to collec…
Simulator and Experience Enhanced Diffusion Model for Comprehensive ECG Generation
Xiaoda Wang, Kaiqiao Han, Yuhao Xu +4
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…
Generalist vs Specialist Time Series Foundation Models: Investigating Potential Emergent Behaviors in Assessing Human Health Using PPG Signals
Saurabh Kataria, Yi Wu, Zhaoliang Chen +21
Foundation models are large-scale machine learning models that are pre-trained on massive amounts of data and can be adapted for various downstream tasks. They have been extensivel…