This study seeks to determine the association between adherence to positive airway pressure (PAP) and healthcare costs among a national sample of older adults with comorbid OSA and common chronic conditions.
This study included standardized skin pigmentation assessments to enable bias analyses in EnsoData’s PPG-Based OSA detection algorithm.
This study highlights the Prospective Clinical Performance Validation of AI for PPG-based Sleep Staging, presented by EnsoData Research.
This study assesses the Impact of Area Socioeconomic Deprivation and Demographic Variables on Machine Learning Models for OSA Treatment.
This study assesses the rate of testing for Obstructive Sleep Apnea analyzed through a lens of socioeconomic demographic data.
This study assesses the rate of treatment for Obstructive Sleep Apnea analyzed through a lens of socioeconomic demographic data.
This study demonstrates potential to improve identification of RBD and RBD subtype-specific EEG biomarkers associated with synucleinopathy and PTSD/TASD.
This study features novel analytic methods for explainability, SBCP (spectral band cluster prevalence), with potential applications to Narcolepsy disorder-specific EEG biomarkers and AI understandability.
This study highlights the relationship between RDW and Disease Burden in OSA, presented by Northwell Health, Donald and Barbara Zucker School of Medicine at Hofstra/Northwel, and EnsoData Research.
This study highlights the use of machine learning based on multi-modal data to predict PAP adherence in patients with OSA, presented by Kaiser Permanente and EnsoData Research.