Summary of Alpec: a Comprehensive Evaluation Framework and Dataset For Machine Learning-based Arousal Detection in Clinical Practice, by Stefan Kraft et al.
ALPEC: A Comprehensive Evaluation Framework and Dataset for Machine Learning-Based Arousal Detection in Clinical Practice
by Stefan Kraft, Andreas Theissler, Vera Wienhausen-Wilke, Philipp Walter, Gjergji Kasneci
First submitted to arxiv on: 20 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 The abstract presents a novel post-processing and evaluation framework for detecting arousals in sleep, addressing fundamental issues in using Machine Learning (ML) in clinical practice. The framework, called ALPEC, emphasizes approximate localization and precise event count of arousals, aligning with clinicians’ annotation protocols. The authors recommend that ML practitioners focus on detecting arousal onsets, demonstrating the impact of this shift on current training and evaluation schemes. A novel comprehensive polysomnographic dataset (CPS) is introduced, reflecting clinical annotation constraints and including modalities not present in existing datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Detecting arousals in sleep is important for diagnosing sleep disorders. However, using Machine Learning (ML) in clinics is difficult because ML methods don’t match how clinicians annotate sleep data. This paper solves this problem by introducing a new way to process and evaluate ML models that detect arousals. The new method, called ALPEC, focuses on finding the start of an arousal, which is what clinicians do when they annotate sleep data. The authors also introduce a new dataset with sleep data from different sensors that reflects how clinicians annotate sleep data. This helps ML models work better in clinics and reduces the gap between technology and clinical needs. |
Keywords
* Artificial intelligence * Machine learning
