Positional sleep apnea phenotyping using machine learning and digital oximetry biomarkers

Study Objectives. To examine the feasibility of using digital oximetry biomarkers (OBMs) and body position to identify positional obstructive sleep apnea (POSA) phenotypes. Methods. A multiclass extreme gradient boost (XGBoost) was implemented to classify between three POSA phenotypes, i.e., positional patients (PP), including supine-predominant OSA (spOSA), and supine-isolated OSA (siOSA), and non-positional patients (NPP). A total of 861 individuals with OSA from the multi ethnic study of atherosclerosis (MESA) dataset were included in the study. Overall, 43 OBMs were computed for supine and non-supine positions and used as input features together with demographic and clinical information (META). Feature selection, using mRMR, was implemented, and nested cross validation was used for the model’s performance evaluation. Results. The best performance for the multiclass classification yielded a median weighted F1 of 0.79 with interquartile range (IQR) of 0.06. Binary classification between PP to NPP achieved weighted F1 of 0.87 (0.04). Conclusion. Using OBMs computed in PP and NPP with OSA, it is possible to distinguish between the different phenotypes of POSA. This data-driven algorithm may be embedded in portable home sleep tests.