RT info:eu-repo/semantics/article
T1 Deep learning for obstructive sleep apnea diagnosis based on single channel oximetry
A1 Levy, Jeremy
A1 Álvarez, Daniel
A1 Del Campo, Félix
A1 Behar, Joachim A.
AB Obstructive sleep apnea (OSA) is a serious medical condition with a high prevalence, although diagnosis remains a challenge. Existing home sleep tests may provide acceptable diagnosis performance but have shown several limitations. In this retrospective study, we used 12,923 polysomnography recordings from six independent databases to develop and evaluate a deep learning model, calledOxiNet, for the estimation of the apnea-hypopnea index from the oximetry signal. We evaluated OxiNet performance across ethnicity, age, sex, and comorbidity. OxiNet missed 0.2% of all test set moderate-tosevere OSA patients against 21% for the best benchmark.
PB SPRINGER NATURE
SN 2041-1723
YR 2023
FD 2023
LK https://uvadoc.uva.es/handle/10324/74130
UL https://uvadoc.uva.es/handle/10324/74130
LA eng
NO Nature Communications, 2023, vol. 14, p. 4881
NO Producción Científica
DS UVaDOC
RD 22-ene-2025