@article{10902/39108,
year = {2025},
month = {2},
url = {https://hdl.handle.net/10902/39108},
abstract = {Obstructive sleep apnea (OSA) in children is a prevalent and serious respiratory condition linked to cardiovascular morbidity. Polysomnography, the standard diagnostic approach, faces challenges in accessibility and complexity, leading to underdiagnosis. To simplify OSA diagnosis, deep learning (DL) algorithms have been developed using cardiac signals, but they often lack interpretability. Our study introduces a novel interpretable DL approach (SleepECG-Net) for directly estimating OSA severity in at-risk children. A combination of convolutional and recurrent neural networks (CNN-RNN) was trained on overnight electrocardiogram (ECG) signals. Gradient-weighted Class Activation Mapping (Grad-CAM), an eXplainable Artificial Intelligence (XAI) algorithm, was applied to explain model decisions and extract ECG patterns relevant to pediatric OSA. Accordingly, ECG signals from the semi-public Childhood Adenotonsillectomy Trial (CHAT, n = 1610) and Cleveland Family Study (CFS,n = 64), and the private University of Chicago (UofC, n = 981) databases were used. OSA diagnostic performance reached 4-class Cohen's Kappa of 0.410, 0.335, and 0.249 in CHAT, UofC, and CFS, respectively. The proposal demonstrated improved performance with increased severity along with heightened cardiovascular risk. XAI findings highlighted the detection of established ECG features linked to OSA, such as bradycardia-tachycardia events and delayed ECG patterns during apnea/hypopnea occurrences, focusing on clusters of events. Furthermore, Grad-CAM heatmaps identified potential ECG patterns indicating cardiovascular risk, such as P, T, and U waves, QT intervals, and QRS complex variations. Hence, SleepECG-Net approach may improve pediatric OSA diagnosis by also offering cardiac risk factor information, thereby increasing clinician confidence in automated systems, and promoting their effective adoption in clinical practice.},
organization = {This work is part of the projects PID2020-115468RB-I00 and CPP2022- 009735, funded by MCIN/AEI/10.13039/501100011033 and the European Union “NextGenerationEU”/PRTR. This research was also co-funded by the European Union through the Interreg VI-A Spain-Portugal Program (POCTEP) 2021-2027 (0043_NET4SLEEP_2_E), and by “CIBER-Consorcio Centro de Investigación Biomédica en Red” (CB19/01/00012) through “Instituto de Salud Carlos III”, co-funded with European Regional Development Fund, as well as under the project TinyHeart from 2022 Early Stage call. The Childhood Adenotonsillectomy Trial (CHAT) was supported by the National Institutes of Health (HL083075, HL083129, UL1-RR-024134, UL1 RR024989). The Cleveland Family Study (CFS) was supported by grants from the National Institutes of Health (HL46380, M01 RR00080-39, T32-HL07567, RO1- 46380). The National Sleep Research Resource was supported by the National Heart, Lung, and Blood Institute (R24 HL114473, 75N92019R002).},
publisher = {Institute of Electrical and Electronics Engineers, Inc.},
publisher = {IEEE Journal of Biomedical and Health Informatics, 2025, 29(2), 1021-1034},
title = {SleepECG-Net: explainable deep learning approach with ECG for pediatric sleep apnea diagnosis},
author = {García Vicente, Clara and Gutiérrez Tobal, Gonzalo César and Vaquerizo Villar, Fernando and Martín Montero, Adrián and Gozal, David and Hornero Sánchez, Roberto},
}