An Explainable Deep-Learning Approach to Detect Pediatric Sleep Apnea From Single-Channel Airflow
This study presents an interpretable deep-learning tool using airflow to accurately detect pediatric obstructive sleep apnea, enabling early, objective diagnosis and supporting clinical decision-making through identification of relevant respiratory patterns.
Authors
- Gonzalo C. Gutiérrez‐Tobal (ORCID: https://orcid.org/0000-0002-1237-3424)
- Fernando Vaquerizo-Villar (ORCID: https://orcid.org/0000-0002-5898-2006)
- Ehab Dayyat
- Roberto Hornero (ORCID: https://orcid.org/0000-0001-9915-2570)
- David Gozal (ORCID: https://orcid.org/0000-0001-8195-6036)
- Verónica Barroso-García (ORCID: https://orcid.org/0000-0001-5648-206X)
- Timo Leppänen (ORCID: https://orcid.org/0000-0003-4017-821X)
Institutions
- Universidad de Valladolid (ES)
- University of Eastern Finland (FI)
- Baylor Scott & White Medical Center - Temple (US)
- Marshall University (US)
Publication Details
- Journal
- IEEE Journal of Translational Engineering in Health and Medicine
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1109/jtehm.2025.3625388
- Primary Topic
- Obstructive Sleep Apnea Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- European Commission
- Kuopion Yliopistollinen Sairaala
- National Institutes of Health
- Instituto de Salud Carlos III
- European Regional Development Fund
- Agencia Estatal de Investigación
- National Heart, Lung, and Blood Institute
- Interreg