RT info:eu-repo/semantics/article
T1 SleepECG-Net: Explainable Deep Learning Approach With ECG for Pediatric Sleep Apnea Diagnosis
A1 García-Vicente, Clara
A1 Gutiérrez-Tobal, Gonzalo C.
A1 Vaquerizo-Villar, Fernando
A1 Martín-Montero, Adrián
A1 Gozal, David
A1 Hornero, Roberto
K1 3325 Tecnología de las Telecomunicaciones
K1 1203.04 Inteligencia Artificial
K1 3314 Tecnología Médica
AB 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.
PB IEEE INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS INC
SN 2168-2194
YR 2025
FD 2025-02-01
LK https://uvadoc.uva.es/handle/10324/81356
UL https://uvadoc.uva.es/handle/10324/81356
LA spa
NO IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, Febrero 2025, vol. 29, n. 2, p. 1021-1034
NO Producción Científica
DS UVaDOC
RD 13-ene-2026