The use of heart rate variability, oxygen saturation, and anthropometric data with machine learning to predict the presence and severity of obstructive sleep apnea
The present study demonstrates the feasibility of using classification models to detect the presence and severity of OSA using these indices. Our findings have the potential to contribute to the development of rapid screening tools aimed at assisting individuals affected by this condition, to expedite diagnosis and initiate timely treatment.
Authors
- Renato Tinós (ORCID: https://orcid.org/0000-0003-4027-8851)
- Rubens Fazan (ORCID: https://orcid.org/0000-0002-6200-6008)
- Álan Luiz Éckeli (ORCID: https://orcid.org/0000-0001-5691-7158)
- Hélio César Salgado (ORCID: https://orcid.org/0000-0001-7351-5740)
- Luiz Eduardo Virgílio Silva (ORCID: https://orcid.org/0000-0003-0801-1209)
- Rafael Rodrigues Dos Santos (ORCID: https://orcid.org/0000-0001-9565-9251)
- M Marumo
Institutions
- Children's Hospital of Philadelphia (US)
- Universidade de São Paulo (BR)
Publication Details
- Journal
- Frontiers in Cardiovascular Medicine
- Published
- 2025-03-14
- DOI
- https://doi.org/10.3389/fcvm.2025.1389402
- Citations
- 2
- Primary Topic
- Obstructive Sleep Apnea Research
- Type
- article
- Field-Weighted Citation Impact
- 2.33