Abdelfattah Mohamed, Zhou Li, Sum-Ping Oliver, Hekmat Anahid, Galati Joanna, Gupta Niraj, Adaimi George, Marwaha Salonee, Parekh Ankit, Mignot Emmanuel, Alahi Alexandre, During Emmanuel
École Polytechnique Fédérale de Lausanne, ENAC IIC Visual Intelligence for Transportation, Lausanne, Switzerland.
Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY.
Ann Neurol. 2025 May;97(5):860-872. doi: 10.1002/ana.27170. Epub 2025 Jan 9.
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-polysomnogram (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.
The dataset included 172 vPSG recordings from a clinical sleep center, 81 patients with iRBD and 91 non-RBD 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.
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 (0.1-2 second duration) movements achieved the highest accuracy at 91.9%. Of the 11 patients with iRBD without noticeable movements during vPSG, 7 were correctly identified.
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. ANN NEUROL 2025;97:860-872.
大多数情况下，孤立性快速眼动（REM）睡眠行为障碍（iRBD）是帕金森病或相关疾病的早期阶段。诊断需要进行整夜视频多导睡眠图（vPSG）检查，然而，即使对于睡眠专家而言，解读vPSG数据也具有挑战性。使用3D摄像头对运动进行自动分析已取得了很高的准确性。我们旨在使用传统的2D摄像头复制并扩展先前的研究工作。
该数据集包括来自一个临床睡眠中心的172份vPSG记录，其中81例iRBD患者和91名非RBD健康对照者（63名患有一系列其他睡眠障碍，28名睡眠正常者）。一种光流计算机视觉算法可自动检测快速眼动（REM）睡眠期间的运动，并从中提取运动的速率、比率、幅度和速度以及不动比率等特征。
iRBD患者表现出较短运动和不动期的数量增加。检测iRBD的准确率范围为84.9%（使用2个特征）至87.2%（使用5个特征）。结合所有5个特征但仅分析短（持续时间为0.1 - 2秒）运动时，准确率最高，为91.9%。在vPSG期间无明显运动的11例iRBD患者中，有7例被正确识别。
这项工作通过使用睡眠实验室常规使用的2D摄像头改进了现有技术，并仅通过添加3个特征提高了性能。这种方法可在临床睡眠实验室中实施，以促进和改善iRBD的诊断。结合REM睡眠的自动检测，还应在家庭环境中使用传统红外摄像头进行测试，以检测和/或监测RBD。《神经病学纪事》2025年；97:860 - 872。
