Beacon Biosignals announced that it received FDA 510(k) clearance for its SleepStageML machine-learning software for sleep monitoring.
SleepStageML automatically stages sleep from electroencephalogram (EEG) signals of clinical polysomnography (PSG) recordings. It aids in the diagnosis and evaluation of sleep and sleep-related disorders.
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Boston-based Beacon Biosignals said in a news release that the software could enable more consistent, efficient and precise sleep staging. The FDA clearance complements its Dreem 3S wearable headband and integrated algorithms. Dreem 3S, an at-home wearable monitoring device, received FDA clearance in September 2023.
“With FDA clearances for both SleepStageML and Dreem 3S headband, Beacon now provides an unparalleled capability to measure sleep physiology whether studies are conducted in-home or in-clinic,” said Dr. Jacob Donoghue, CEO of Beacon Biosignals. “Beacon’s powerful analytics platform allows for rich analysis of clinical datasets across multiple environments to spur innovation in therapies for sleep disorders as well as neurological and psychiatric conditions with sleep comorbidities.”
SleepStageML leverages an advanced deep-learning model to robustly score sleep stages. Beacon Biosignals trained the model on a large dataset with hundreds of thousands of hours of PSG recordings. These recordings came from both healthy individuals and individuals with a diverse set of sleep disorders and neurologic and psychiatric diseases.
Key benefits of SleepStageML include automated sleep staging, reduced variability in scoring and faster PSG analysis turnaround.
Beacon Biosignals also has the benefit of developing its algorithms under the FDA’s Predetermined Change Control Plan (PCCP). This allows the company to continuously tweak and improve its algorithm while operating under the initial 510(k) clearance.
“SleepStageML’s approved PCCP is a game-changing development for the sleep field,” said Alexander Chan, VP of analytics and machine learning at Beacon Biosignals. “With this regulatory pathway, we can provide even more accurate and robust sleep staging capabilities over time. This ability to iteratively enhance SleepStageML will be invaluable for generating insights to accelerate sleep therapy research and development.”
