Obstructive Sleep Apnea (OSA) is a prevalent sleep disorder that significantly affects public health, contributing to cardiovascular and metabolic impairments. Previous studies highlight the heterogeneity of OSA, which is manifested in different phenotypes, complicating personalized treatment strategies. Current phenotyping methods primarily rely on traditional clustering techniques, such as k-means, which may fail to capture complex relationships among features. This study introduces a novel approach based on subject-based SpO2 weighted correlation networks and modularity analysis to identify clinically relevant subgroups within the OSA population. Using a subset of 2,641 subjects from the Sleep Heart Health Study (SHHS), we extracted 43 SpO2 features from polysomnography to build correlation networks from them. A bootstrap procedure ensured robustness, while Blondel's modularity algorithm identified subgroups without requiring a predefined number of clusters. Comparison with k-means revealed that the correlation network method identified subgroups with more significantly different sociodemographic, clinical, and anthropometric characteristics (35 variables vs. 28 for k-means). These 35 features effectively revealed hidden SpO2 patterns, suggesting that subject-based correlation networks can identify distinct OSA phenotypes and enhance personalized treatment strategies. This approach improves clinical decision-making and patient care. Future research should validate these findings in longitudinal studies and explore integrating multimodal data to refine OSA phenotyping.Clinical Relevance- This study introduces a novel clustering method based on subject-based weighted correlation networks, offering a precise approach to identifying phenotypic subgroups in obstructive sleep apnea on the sole basis of SpO2 data, thus enabling tailored interventions.
