Research - EnsoData

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.

Prospective Clinical Performance Validation of AI for PPG-based Sleep Staging

This study highlights the Prospective Clinical Performance Validation of AI for PPG-based Sleep Staging, presented by EnsoData Research.

Impact of Area Socioeconomic Deprivation and Demographic Variables on Machine Learning Models for OSA Treatment

This study assesses the Impact of Area Socioeconomic Deprivation and Demographic Variables on Machine Learning Models for OSA Treatment.

Testing for Obstructive Sleep Apnea by Area Socioeconomic Deprivation in Six Million Adults

This study assesses the rate of testing for Obstructive Sleep Apnea analyzed through a lens of socioeconomic demographic data.

Treatment for Obstructive Sleep Apnea by Area Socioeconomic Deprivation in Six Million Adults

This study assesses the rate of treatment for Obstructive Sleep Apnea analyzed through a lens of socioeconomic demographic data.

REM Behavior Disorder Explainability in EEG via Spectral Band Cluster Prevalence

This study demonstrates potential to improve identification of RBD and RBD subtype-specific EEG biomarkers associated with synucleinopathy and PTSD/TASD.

Narcolepsy Disorders Explainability in EEG via Spectral Band Cluster Prevalence

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.

Relationship Between RDW and Disease Burden in Obstructive Sleep Apnea

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.

Utilizing machine learning based on multi-modal data to predict PAP adherence in patients with OSA

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.