Original from: Labpulse.com

EnsoData has received 510(k) clearance from the U.S. Food and Drug Administration (FDA) for its machine learning-based diagnostic for sleep apnea and other sleep disorders that uses data from pulse oximetry (pulse ox) devices.

The diagnostic, EnsoSleep PPG, runs on EnsoSleep, the firm’s cloud-based software platform. The EnsoSleep system was previously cleared by the FDA in 2017 and expanded in 2021; it uses artificial intelligence (AI) to analyze data compiled through traditional sleep studies using in-lab polysomnography and home sleep apnea testing to assist in diagnosing sleep apnea and other sleep disorders. The EnsoSleep PPG uses AI-driven analysis of photoplethysmogram (PPG) signals recorded by pulse oximeters.

Pulse ox devices record physiological data such as a patient's oxygen saturation levels and heart rate during sleep studies; they have the advantages of being wearable and much less expensive than older sleep diagnostic equipment.'

EnsoSleep PPG uses deep-learning models to analyze and score the data from pulse ox devices to detect sleep stages, disordered breathing events such as apneas or hypopneas, and other sleep measures, and generate a sleep study report.

"Our interoperable AI tools are democratizing the ability to accurately measure sleep and aid in diagnosis of sleep disorders broadly -- for the existing category of FDA-cleared pulse oximetry devices and sensors that are already widely deployed, in-use clinically, and growing in their adoption daily. With PPGs among the most commonplace of medical waveforms collected across healthcare settings, from diagnostic tests to bedside monitors and consumer wearables, this will be transformative for patient access and outcomes to achieve better sleep and overall health," said Chris Fernandez, EnsoData’s co-founder and chief research officer.