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sleep-events-detection

https://github.com/adrania/sleep-events-detection

Science Score: 57.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
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    .zenodo.json file
    Found .zenodo.json file
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    DOI references
    Found 1 DOI reference(s) in README
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    Academic publication links
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    Institutional organization owner
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    JOSS paper metadata
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    Scientific vocabulary similarity
    Low similarity (1.8%) to scientific vocabulary
Last synced: 6 months ago · JSON representation ·

Repository

Basic Info
  • Host: GitHub
  • Owner: adrania
  • Language: Python
  • Default Branch: main
  • Size: 34.2 KB
Statistics
  • Stars: 3
  • Watchers: 2
  • Forks: 0
  • Open Issues: 0
  • Releases: 1
Created about 1 year ago · Last pushed 9 months ago
Metadata Files
Readme Citation

README.md

Multi-task deep-learning for sleep event detection and stage classification

Source code for reproducibility of experiments described in the following paper:

A. Anido-Alonso and D. Alvarez-Estevez, "Multi-Task Deep-Learning for Sleep Event Detection and Stage Classification," 2025 IEEE Symposium on Computational Intelligence in Health and Medicine Companion (CIHM Companion), Trondheim, Norway, 2025, pp. 1-5, doi: 10.1109/CIHMCompanion65205.2025.11002700

Owner

  • Name: Adriana
  • Login: adrania
  • Kind: user
  • Location: Spain

Biologist specialized in Biotechnology and Bioinformatics. Getting started in programming.

Citation (CITATION.bib) 

@INPROCEEDINGS{11002700,
  author={Anido-Alonso, Adriana and Alvarez-Estevez, Diego},
  booktitle={2025 IEEE Symposium on Computational Intelligence in Health and Medicine Companion (CIHM Companion)}, 
  title={Multi-Task Deep-Learning for Sleep Event Detection and Stage Classification}, 
  year={2025},
  volume={},
  number={},
  pages={1-5},
  keywords={Visualization;Computer vision;Sleep;Event detection;Focusing;Manuals;Multitasking;Electroencephalography;Iterative methods;Standards;automatic sleep scoring;deep-learning;EEG arousal;multi-task learning;respiratory events;sleep stages},
  doi={10.1109/CIHMCompanion65205.2025.11002700}}

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Last Year
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