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Deep learning for obstructive sleep apnea diagnosis based on single channel oximetry.
Levy, Jeremy; Álvarez, Daniel; Del Campo, Félix; Behar, Joachim A.
Afiliación
  • Levy J; The Andrew and Erna Viterbi Faculty of Electrical & Computer Engineering, Technion-IIT, Haifa, Israel.
  • Álvarez D; Faculty of Biomedical Engineering, Technion, Israel Institute of Technology, Haifa, Israel.
  • Del Campo F; Río Hortega University Hospital Valladolid, Valladolid, Spain.
  • Behar JA; Biomedical Engineering Group, University of Valladolid, Valladolid, Spain.
Nat Commun ; 14(1): 4881, 2023 08 12.
Article en En | MEDLINE | ID: mdl-37573327
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, called OxiNet, 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-to-severe OSA patients against 21% for the best benchmark.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Apnea Obstructiva del Sueño / Aprendizaje Profundo Tipo de estudio: Diagnostic_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Nat Commun Asunto de la revista: BIOLOGIA / CIENCIA Año: 2023 Tipo del documento: Article País de afiliación: Israel Pais de publicación: Reino Unido
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Apnea Obstructiva del Sueño / Aprendizaje Profundo Tipo de estudio: Diagnostic_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Nat Commun Asunto de la revista: BIOLOGIA / CIENCIA Año: 2023 Tipo del documento: Article País de afiliación: Israel Pais de publicación: Reino Unido