Summary of Deep Learning For Identifying Systolic Complexes in Scg Traces: a Cross-dataset Analysis, by Michele Craighero et al.
Deep Learning for identifying systolic complexes in SCG traces: a cross-dataset analysis
by Michele Craighero, Sarah Solbiati, Federica Mozzini, Enrico Caiani, Giacomo Boracchi
First submitted to arxiv on: 8 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 In this paper, researchers investigate the use of seismocardiographic signals to analyze cardiac activity, focusing on the systolic complex as the most informative part. They build upon existing deep learning models, which have been shown effective in controlled scenarios using a single dataset. However, they acknowledge that real-world scenarios often involve domain shifts and data distribution changes. To address this, they propose a personalized approach to contrast these shifts and demonstrate the benefits of multi-channel analysis incorporating accelerometer and gyroscope data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores the potential of seismocardiography for cardiac activity analysis, using deep learning models. It highlights the importance of considering real-world scenarios, which often involve domain shifts in data distribution. The authors propose a personalized approach to address these changes and show that multi-channel analysis can provide valuable insights. |
Keywords
* Artificial intelligence * Deep learning
