Positional sleep apnea phenotyping using machine learning and digital oximetry biomarkers ^*

Abstract

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.

Patent and Bibliometric Analysis of the Scientific Landscape of the Use of Pulse Oximeters and Their Prospects in the Field of Digital Medicine

“…The implementation of digital technologies while using pulse oximeters is also promising, including remote monitoring, and artificial intelligence is an area of research interest today in major scientific centers around the world. This diagnostic direction, in the full sense of the word, is an evolving scientific frontier [27][28][29][30]. This study's objective was to conduct a patent and bibliometric analysis of the scientific landscape of contemporary innovative technological solutions for the development and application of pulse oximeters, including in the field of digital medicine, and identify technical solutions to reduce the risks associated with the use of pulse oximetry.…”