Yingjing Feng
Papers
1
Total Citations
16
H-Index
1
About
Dr. Yingjing Feng is a leading researcher in computational electrophysiology and interventional cardiology, with a focus on optimizing cardiac mapping for radiofrequency catheter ablation. Their most cited work, "An efficient cardiac mapping strategy for radiofrequency catheter ablation with active learning" (2017, 16 citations), introduces a novel framework that learns from expert electrophysiologists while employing active-learning algorithms to guide mapping in real time. This approach significantly reduces the number of measurement points needed to reconstruct accurate voltage and activation maps, addressing a critical bottleneck in time-constrained ablation procedures. By integrating machine learning with clinical expertise, Feng’s work bridges the gap between artificial intelligence and practical cardiac intervention. Though early in their career, this contribution has already garnered attention for its potential to improve procedural efficiency and patient outcomes. Feng’s research stands at the intersection of biomedical engineering and data-driven clinical decision support, offering a promising pathway toward smarter, faster, and more precise catheter ablation strategies.
Research Focus
Key Achievements
Top Papers
- 1