Possti Daniel, Oz Shani, Gerston Aaron, Wasserman Danielle, Duncan Iain, Cesari Matteo, Dagay Andrew, Tauman Riva, Mirelman Anat, Hanein Yael
X-trodes, Herzelia, Israel.
School of Electrical Engineering, Tel Aviv University, Tel Aviv, Israel.
NPJ Digit Med. 2024 Nov 28;7(1):341. doi: 10.1038/s41746-024-01354-8.
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中的金标准诊断工具，在人工环境中进行。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敏感，但微观结构则更为稳定。在本研究中，我们应用半自动算法来捕捉无张力快速眼动睡眠（RSWA）和睡眠纺锤波，比较实验室测量和家庭测量。我们分析了来自55名受试者的107份全夜记录：24名健康成年人、28名帕金森病患者（15名有快速眼动睡眠行为障碍）和3名患有孤立性快速眼动睡眠行为障碍（RBD）的患者。记录由人工进行评分。开发了一种用于量化RSWA的自动算法，并与人工评分进行对比测试。RSWAi显示家庭测量和实验室测量之间的相关性为60%。RBD检测的灵敏度达到83%，特异性为79%，平衡准确率为81%。该算法准确地量化了RSWA，能够检测出RBD患者。这些发现可能有助于更便捷的睡眠测试，并为RBD筛查提供一种可能的替代方法。
