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Prospective Clinical Validation of AI for PPG-based OSA detection utilizing Standardized Skin Color Assessments
This study included standardized skin pigmentation assessments to enable bias analyses in EnsoData’s PPG-Based OSA detection algorithm.
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 research study evaluates large multi-site datasets and assesses the relationship of N3/REM sleep duration with the predicted brain age.
In this study, EnsoData shows how ML algorithms based on PAP usage can predict future adherence, offering potential for personalized treatment decisions and preemptive interventions when upcoming non-adherence is predicted.
This research study demonstrated that Machine Learning methods can automatically detect Type I Narcolepsy using in PSG-EEG with promising degrees of accuracy.