Summary of Self-trained Model For Ecg Complex Delineation, by Aram Avetisyan et al.
Self-Trained Model for ECG Complex Delineation
by Aram Avetisyan, Nikolas Khachaturov, Ariana Asatryan, Shahane Tigranyan, Yury Markin
First submitted to arxiv on: 4 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Signal Processing (eess.SP)
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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 A novel self-trained approach is introduced to improve electrocardiogram (ECG) delineation, which is crucial for accurate diagnoses in cardiology. The authors propose a method that leverages vast amounts of unlabeled ECG data by pseudolabeling it using a neural network trained on a dataset specifically designed for ECG delineation. This approach enhances the quality of delineation, outperforming existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers developed a new way to accurately draw boundaries around heart rhythms in electrocardiograms (ECGs). This is important because cardiologists need these precise boundaries to make accurate diagnoses. The new method uses a big amount of data that isn’t labeled yet and makes educated guesses about what the correct labels would be. Then, it trains a model on those labeled samples to get even better at drawing those boundaries. |
Keywords
» Artificial intelligence » Neural network
