Polysomnography, the gold standard diagnostic tool in sleep medicine, is performed in an artificial environment. This might alter sleep and may not accurately reflect typical sleep patterns. While macro-structures are sensitive to environmental effects, micro-structures remain more stable. In this study we applied semi-automated algorithms to capture REM sleep without atonia (RSWA) and sleep spindles, comparing lab and home measurements. We analyzed 107 full-night recordings from 55 subjects: 24 healthy adults, 28 Parkinson’s disease patients (15 RBD), and three with isolated Rem sleep behavior disorder (RBD). Sessions were manually scored. An automatic algorithm for quantifying RSWA was developed and tested against manual scoring. RSWAi showed a 60% correlation between home and lab. RBD detection achieved 83% sensitivity, 79% specificity, and 81% balanced accuracy. The algorithm accurately quantified RSWA, enabling the detection of RBD patients. These findings could facilitate more accessible sleep testing, and provide a possible alternative for screening RBD.

多导睡眠图是睡眠医学的金标准诊断工具，是在人工环境中进行的。这可能会改变睡眠，并且可能无法准确反映典型的睡眠模式。虽然宏观结构对环境影响很敏感，但微观结构仍然更加稳定。在这项研究中，我们应用半自动算法来捕获无失张力的 REM 睡眠 （RSWA） 和睡眠纺锤波，比较实验室和家庭测量。我们分析了来自 55 名受试者的 107 份整夜记录： 24 名健康成人，28 名帕金森病患者 （15 名 RBD） 和 3 名孤立性 Rem 睡眠行为障碍 （RBD）。会话是手动评分的。开发了一种量化 RSWA 的自动算法，并针对手动评分进行了测试。RSWAi 显示家庭和实验室之间存在 60% 的相关性。RBD 检测实现了 83% 的灵敏度、79% 的特异性和 81% 的平衡准确性。该算法准确量化了 RSWA，从而能够检测 RBD 患者。这些发现可能有助于更易获得的睡眠测试，并为筛查 RBD 提供可能的替代方案。