ECG-based artificial intelligence for obstructive sleep apnea detection: a mechanism-oriented diagnostic...

Artificial intelligence (AI)-based approaches have shown promising performance in the detection of obstructive sleep apnea (OSA) using electrocardiography (ECG)-based physiological signals. However, existing studies primarily focus on diagnostic accuracy, while the underlying physiological mechanisms driving AI-based detection remain largely unexplored. This study aimed to evaluate the diagnostic performance of segment-level AI models and to provide a mechanism-oriented interpretation of their physiological basis.

A systematic review and diagnostic meta-analysis were conducted in accordance with PRISMA 2020 and PRISMA-DTA guidelines. PubMed, Scopus, Web of Science, and IEEE Xplore databases were systematically searched. Studies utilizing AI models for segment-level OSA detection based on physiological signals were included. Sensitivity and specificity values were extracted, and 2 × 2 contingency tables were reconstructed using a standardized approach to enable cross-study comparability. A bivariate random-effects model was applied, and summary receiver operating characteristic (SROC) curves were generated.

Thirteen studies met the inclusion criteria. The pooled sensitivity and specificity were 0.88 and 0.89, respectively, indicating high diagnostic performance. The SROC curve demonstrated excellent discriminative ability (AUC > 0.90), with moderate between-study heterogeneity. Notably, consistent performance across diverse model architectures suggests that AI models rely on stable physiological signal patterns rather than dataset-specific features.

AI-based models using physiological signals achieve high diagnostic accuracy for segment-level OSA detection. Importantly, these findings suggest that AI systems may not directly detect airway obstruction but instead may identify downstream physiological responses, particularly autonomic nervous system alterations reflected in cardiovascular signals.