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Evaluation of Machine-Learning Approaches to Estimate Sleep Apnea Severity From At-Home Oximetry Recordings

  • Published:2019-03 Issue:2 Volume:23 Page:882-892
  • ISSN:2168-2194
  • Container-title:IEEE Journal of Biomedical and Health Informatics
  • language:
  • Short-container-title:IEEE J. Biomed. Health Inform.

Author:

Gutierrez-Tobal Gonzalo C.ORCID,Alvarez DanielORCID,Crespo Andrea,del Campo FelixORCID,Hornero RobertoORCID

Funder

Ministerio de Economia y Competitividad

Sociedad Espacola de Neumologia y Cirugia Torocica

European Regional Development Fund

Consejeria de Educacion Junta de Castilla y Leon

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Health Information Management,Electrical and Electronic Engineering,Computer Science Applications,Biotechnology

Cited by 56 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Predictive Data Analytics in Telecare and Telehealth: Systematic Scoping Review;Online Journal of Public Health Informatics;2024-08-07

2. Wireless wearable sensors can facilitate rapid detection of sleep apnea in hospitalized stroke patients;SLEEP;2024-05-30

3. Early Detection And Prediction Of Sleep Apnoea Using Deep Learning Techniques;2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems (ADICS);2024-04-18

4. A Review on Automated Sleep Study;Annals of Biomedical Engineering;2024-03-16

5. Predictive Data Analytics in Telecare and Telehealth: Systematic Scoping Review (Preprint);2024-02-21
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