{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T18:14:23Z","timestamp":1791224063555,"version":"4.1.0"},"reference-count":24,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2025,1,9]],"date-time":"2025-01-09T00:00:00Z","timestamp":1736380800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Annals of Neurology"],"published-print":{"date-parts":[[2025,5]]},"abstract":"<jats:sec><jats:title>Objective<\/jats:title><jats:p>Isolated rapid eye movement (REM) sleep behavior disorder (iRBD) is, in most cases, an early stage of Parkinson's disease or related disorders. Diagnosis requires an overnight video\u2010polysomnogram (vPSG), however, even for sleep experts, interpreting vPSG data is challenging. Using a 3D camera, automated analysis of movements has yielded high accuracy. We aimed to replicate and extend prior work using a conventional 2D camera.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>The dataset included 172 vPSG recordings from a clinical sleep center, 81 patients with iRBD and 91 non\u2010RBD healthy controls (63 with a range of other sleep disorders and 28 healthy sleepers). An optical flow computer vision algorithm automatically detected movements during rapid eye movement (REM) sleep, from which features of rate, ratio, magnitude and velocity of movements, and ratio of immobility were extracted.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Patients with iRBD exhibited an increased number of shorter movements and immobility periods. Accuracies for detecting iRBD ranged from 84.9% (with 2 features) to 87.2% (with 5 features). Combining all 5 features but only analyzing short (0.1\u20132\u2009second duration) movements achieved the highest accuracy at 91.9%. Of the 11 patients with iRBD without noticeable movements during vPSG, 7 were correctly identified.<\/jats:p><\/jats:sec><jats:sec><jats:title>Interpretation<\/jats:title><jats:p>This work improves prior art by using a 2D camera routinely used in sleep laboratories and improving performance by adding only 3 features. This approach could be implemented in clinical sleep laboratories to facilitate and improve the diagnosis of iRBD. Coupled with automated detection of REM sleep, it should also be tested in the home environment using conventional infrared cameras to detect and\/or monitor RBD. 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