Source: Frontiers in Cardiovascular Medicine. Unidades: FMRP, FFCLRP
Subjects: APNEIA DO SONO, FREQUÊNCIA CARDÍACA
ABNT
SANTOS, Rafael Rodrigues dos et al. The use of heart rate variability, oxygen saturation, and anthropometric data with machine learning to predict the presence and severity of obstructive sleep apnea. Frontiers in Cardiovascular Medicine, v. 12, p. 1-10, 2025Tradução . . Disponível em: https://doi.org/10.3389/fcvm.2025.1389402. Acesso em: 27 jan. 2026.APA
Santos, R. R. dos, Marumo, M. B., Eckeli, A. L., Salgado, H. C., Silva, L. E. V. da, Tinós, R., & Fazan Júnior, R. (2025). The use of heart rate variability, oxygen saturation, and anthropometric data with machine learning to predict the presence and severity of obstructive sleep apnea. Frontiers in Cardiovascular Medicine, 12, 1-10. doi:10.3389/fcvm.2025.1389402NLM
Santos RR dos, Marumo MB, Eckeli AL, Salgado HC, Silva LEV da, Tinós R, Fazan Júnior R. The use of heart rate variability, oxygen saturation, and anthropometric data with machine learning to predict the presence and severity of obstructive sleep apnea [Internet]. Frontiers in Cardiovascular Medicine. 2025 ; 12 1-10.[citado 2026 jan. 27 ] Available from: https://doi.org/10.3389/fcvm.2025.1389402Vancouver
Santos RR dos, Marumo MB, Eckeli AL, Salgado HC, Silva LEV da, Tinós R, Fazan Júnior R. The use of heart rate variability, oxygen saturation, and anthropometric data with machine learning to predict the presence and severity of obstructive sleep apnea [Internet]. Frontiers in Cardiovascular Medicine. 2025 ; 12 1-10.[citado 2026 jan. 27 ] Available from: https://doi.org/10.3389/fcvm.2025.1389402
