RT info:eu-repo/semantics/article
T1 An Explainable Deep-Learning Approach to Detect Pediatric Sleep Apnea From Single-Channel Airflow
A1 Barroso-García, Verónica
A1 Vaquerizo-Villar, Fernando
A1 Gutiérrez-Tobal, Gonzalo C.
A1 Dayyat, Ehab
A1 Gozal, David
A1 Leppänen, Timo
A1 Hornero, Roberto
K1 Airflow
K1 children
K1 convolutional neural network (CNN)
K1 deep-learning (DL)
K1 explainable artificial intelligence (XAI)
K1 obstructive sleep apnea (OSA)
K1 1203.04 Inteligencia Artificial
K1 3325 Tecnología de las Telecomunicaciones
K1 3314 Tecnología Médica
AB 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.
PB IEEE INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS INC
SN 2168-2372
YR 2025
FD 2025-10-24
LK https://uvadoc.uva.es/handle/10324/80306
UL https://uvadoc.uva.es/handle/10324/80306
LA spa
NO IEEE Journal of Translational Engineering in Health and Medicine, Octubre 2025, vol. 13, 517-531
NO Producción Científica
DS UVaDOC
RD 04-dic-2025