Semi automatic quantification of REM sleep without atonia in natural sleep environment

IF 12.4 1区医学 Q1 HEALTH CARE SCIENCES & SERVICES

NPJ Digital Medicine Pub Date : 2024-11-28 DOI:10.1038/s41746-024-01354-8

Daniel Possti, Shani Oz, Aaron Gerston, Danielle Wasserman, Iain Duncan, Matteo Cesari, Andrew Dagay, Riva Tauman, Anat Mirelman, Yael Hanein

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引用次数: 0

Abstract

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.

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在自然睡眠环境中对无失眠的快速眼动睡眠进行半自动量化

多导睡眠图是睡眠医学的金标准诊断工具，在人工环境中进行。这可能会改变睡眠，也可能无法准确反映典型的睡眠模式。宏观结构对环境影响很敏感，而微观结构则更为稳定。在这项研究中，我们采用半自动化算法捕捉无失张的快速眼动睡眠（RSWA）和睡眠棘波，并对实验室和家庭测量结果进行了比较。我们分析了 55 名受试者的 107 个整夜记录：24 名健康成人、28 名帕金森病患者（15 名 RBD）和 3 名孤立的雷姆睡眠行为障碍患者（RBD）。每段录音均由人工评分。开发了一种量化 RSWA 的自动算法，并与人工评分进行了对比测试。RSWAi在家庭和实验室之间显示出60%的相关性。RBD 检测的灵敏度为 83%，特异度为 79%，平衡准确度为 81%。该算法能准确量化 RSWA，从而检测出 RBD 患者。这些研究结果可以让更多人接受睡眠测试，并为筛查 RBD 提供了一种可能的替代方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

NPJ Digital Medicine Multiple-

CiteScore

25.10

自引率

3.30%

发文量

170

审稿时长

15 weeks

期刊介绍： npj Digital Medicine is an online open-access journal that focuses on publishing peer-reviewed research in the field of digital medicine. The journal covers various aspects of digital medicine, including the application and implementation of digital and mobile technologies in clinical settings, virtual healthcare, and the use of artificial intelligence and informatics. The primary goal of the journal is to support innovation and the advancement of healthcare through the integration of new digital and mobile technologies. When determining if a manuscript is suitable for publication, the journal considers four important criteria: novelty, clinical relevance, scientific rigor, and digital innovation.