An open-source, high-performance tool for automated sleep staging

• DOI: 10.7554/elife.70092
• Publication Date: 2021-10-14T11:00:56Z
• Authors: Raphael Vallat; Matthew P Walker
• Keywords: Adult; Male; 0301 basic medicine; Adolescent; QH301-705.5; Science; Polysomnography; Automation; 03 medical and health sciences; Predictive Value of Tests; automated sleep staging; Humans; sleep scoring; Biology (General); Child; Aged; Randomized Controlled Trials as Topic; Aged, 80 and over; Observer Variation; algorithm; YASA; Electromyography; machine-learning; Q; R; Brain; Electroencephalography; Middle Aged; 3. Good health; Electrooculography; Case-Control Studies; NREM sleep; Medicine; Female; Algorithms; Neuroscience

The clinical and societal measurement of human sleep has increased exponentially in recent years. However, unlike other fields of medical analysis that have become highly automated, basic and clinical sleep research still relies on human visual scoring. Such human-based evaluations are time-consuming, tedious, and can be prone to subjective bias. Here, we describe a novel algorithm trained and validated on +30,000 hr of polysomnographic sleep recordings across heterogeneous populations around the world. This tool offers high sleep-staging accuracy that matches human scoring accuracy and interscorer agreement no matter the population kind. The software is designed to be especially easy to use, computationally low-demanding, open source, and free. Our hope is that this software facilitates the broad adoption of an industry-standard automated sleep staging software package.