Combined explainable deep learning model to predict pediatric sleep apnea from ECG and SpO2.

Combining deep learning (DL) with eXplainable Artificial Intelligence (XAI) techniques has led to clinically applicable models that simplify the diagnosis of pediatric obstructive sleep apnea (OSA) using a restricted number of cardiorespiratory signals. However, no prior study has applied these techniques to concurrently analyze electrocardiogram (ECG) and oxygen saturation (SpO2) data. Here, we present an explainable DL approach integrating convolutional neural networks with overnight SpO2 and ECG signals to identify pediatric OSA. SHapley Additive exPlanations (SHAP) XAI technique was used to extract relevant patterns linked to pediatric OSA and explain the model decisions. Patients (n = 3,320) from the semi-public Childhood Adenotonsillectomy Trial (CHAT) and Pediatric Adenotonsillectomy Trial for Snoring (PATS), and the private University of Chicago (UofC) databases were analyzed. Performance obtained Cohen's 4-class kappa of 0.549, 0.457, and 0.378 in CHAT, PATS, and UofC, respectively. Shapley values increased with OSA severity and highlighted the complementarity of SpO2 and ECG, with SpO2 being more relevant in moderate and severe cases and ECG in mild or no OSA cases. SHAP visualizations identified SpO2 desaturations linked to clusters of apneic events and those occurring independently. It also highlighted bradycardia-tachycardia and ECG cardiovascular risk patterns, including variations in P and T waves, PQ and QT intervals, and the QRS complex. Shapley values identified correlations between respiratory and cardiac patterns, showing that desaturations in OSA are linked to cardiac changes. Therefore, our interpretable DL approach may improve pediatric OSA diagnosis by integrating breathing information and accompanying cardiac changes, supporting its effective adoption in clinical settings.