Summary of Combining Hough Transform and Deep Learning Approaches to Reconstruct Ecg Signals From Printouts, by Felix Krones and Ben Walker and Terry Lyons and Adam Mahdi
Combining Hough Transform and Deep Learning Approaches to Reconstruct ECG Signals From Printouts
by Felix Krones, Ben Walker, Terry Lyons, Adam Mahdi
First submitted to arxiv on: 18 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Image and Video Processing (eess.IV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Our approach combines a diverse training set with techniques like Hough transform, U-Net based segmentation, and mask vectorization to reconstruct ECG signals from printouts. We assess our models’ performance using the 10-fold stratified cross-validation (CV) split of the PTB-XL dataset. Our model achieves an average CV signal-to-noise ratio of 17.02 and an official Challenge score of 12.15 on the hidden set, securing first place in the competition. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper’s team, SignalSavants, won the 2024 George B. Moody PhysioNet Challenge by developing a method to reconstruct ECG signals from printouts. The goal is important because many ECGs are still recorded on paper worldwide. Digitizing them could help create more diverse datasets and enable automated analyses. But there’s a catch: different recording standards and image quality can make it hard for models to generalize well. |
Keywords
» Artificial intelligence » Mask » Vectorization
