This study suggests brain age is “malleable” and responsive to therapies that improve sleep and thus highlights its value as a potential biomarker of brain health.
The results of this study highlight the ability to expand OSA screening using ECG-based machine learning systems.
This study evaluates the Impact of Multi-Night Home Sleep Apnea Testing for Obstructive Sleep Apnea (OSA) Diagnosis.
This study explored whether PPG-based sleep stages are robust enough to detect sleep architectural abnormalities specifically associated with Narcolepsy Type-1 (NT1).
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.
Andrea Ramberg, RPSGT, CCSH EnsoData Clinical Director
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