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oai:uvadoc.uva.es:10324/803062026-04-08T12:32:04Zcom_10324_23459com_10324_954com_10324_894col_10324_23460

2025-12-04T12:00:11Z urn:hdl:10324/80306 An Explainable Deep-Learning Approach to Detect Pediatric Sleep Apnea From Single-Channel Airflow Barroso García, Verónica Vaquerizo Villar, Fernando Gutierrez Tobal, Gonzalo César Dayyat, Ehab Gozal, David Leppänen, Timo Hornero Sánchez, Roberto Producción Científica Objective: Approaches based on a single-channel airflow has shown great potential for simplifying pediatric obstructive sleep apnea (OSA) diagnosis. However, analysis has been limited to feature-engineering techniques, restricting identification of complex respiratory patterns, and reducing diagnostic performance in automated models. Here, we propose deep-learning and explainable artificial intelligence (XAI) to estimate the pediatric OSA severity from airflow, while ensuring transparency in automatic decisions. Technology or Method: We used 3,672 overnight airflow recordings from four pediatric datasets. A convolutional neural network (CNN)-based regression model was trained to estimate the apnea-hypopnea index (AHI) and predict OSA severity. We evaluated and compared Gradient-Weighted Class Activation Mapping (Grad-CAM) and SHapley Additive exPlanations (SHAP) to identify the airflow regions where the CNN focuses for predictions. Results: The proposed model demonstrated high concordance between the actual and estimated AHI (intraclass correlation coefficient from 0.69 to 0.87 in the test group), and high diagnostic performance: four-class Cohen’s kappa between 0.37 and 0.43 and accuracies of 82.03%, 97.09%, and 99.03% for three OSA severity cutoffs (i.e. 1, 5, and 10 e/h) in the test group. The interpretability analysis with Grad-CAM and SHAP revealed that the CNN accurately identifies apneic events by focusing on their onset and offset. Both techniques provided complementary information about the model’s decision-making. While Grad-CAM highlighted respiratory events with abrupt signal changes, SHAP captured more subtle patterns with noise included. Conclusions: Accordingly, our model can help automatically detect pediatric OSA and offers clinicians an explainable approach that enhances credibility and usability, thus providing a path toward clinical translation in early diagnosis. Clinical Impact: 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. 2025-12-04T12:00:11Z 2025-12-04T12:00:11Z 2025 info:eu-repo/semantics/article IEEE Journal of Translational Engineering in Health and Medicine, Octubre 2025, vol. 13, 517-531 2168-2372 https://uvadoc.uva.es/handle/10324/80306 10.1109/JTEHM.2025.3625388 517 531 IEEE Journal of Translational Engineering in Health and Medicine 13 2168-2372 spa https://ieeexplore.ieee.org/document/11216356 info:eu-repo/semantics/openAccess © 2025 The Authors. This work is licensed under a Creative Commons Attribution 4.0 License. IEEE INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS INC