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Updated: Jun 23, 2025

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Multi-Modal Home Sleep Monitoring in Older Adults
Published on: January 26, 2019
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在家诊断OSA和失眠仅基于一个Actigraphy总睡眠时间和RIP带的算法"Nox Body SleepTM"
Damien Leger1,2, Maxime Elbaz1,2
1Université Paris Cité, (VIFASOM), ERC 7330 VIgilance FAtigue SOMmeil, Paris, France.
Nature and science of sleep
|June 24, 2024
概括
一个新的人工智能 (AI) 算法Nox BodySleepTM (NBS) 准确评估睡眠状态和阻塞性睡眠呼吸暂停 (OSA) 的严重程度. 这种人工智能工具与多睡眠学 (PSG) 手动评分有很高的一致性,为睡眠分析提供了可靠的替代方案.
科学领域:
- 睡眠医学 睡眠医学
- 医疗保健中的人工智能
- 呼吸系统医学 呼吸系统医学
背景情况:
- 由于COVID-19的流行,医院的消毒必须更加严格,这影响了临床睡眠研究的协议.
- 传统的多睡眠学 (PSG) 需要手动评分,这可能是耗时和资源密集的.
- 需要自动化和可靠的睡眠分析方法,特别是对于像阻塞性睡眠呼吸暂停 (OSA) 这样的疾病.
研究的目的:
- 评估一种新型的人工智能 (AI) 算法Nox BodySleepTM (NBS) 在分析睡眠状态方面的性能.
- 为了比较NBS算法的睡眠分阶段和阻塞性睡眠呼吸暂停 (OSA) 严重性评估与手动多睡眠学 (PSG) 评分.
- 根据新的医院消毒指南,确定NBS算法对OSA和慢性失眠 (CI) 患者的适用性.
主要方法:
- 诺克斯BodySleepTM (NBS) 算法,一个反复的神经网络,被用来估计睡眠状态 (清醒,NREM,REM) 从活动和呼吸诱导囊镜 (RIP) 皮带信号.
- 来自139个PSG研究的睡眠数据 (72个CI,67个OSA) 被NBS分析并与手动PSG评分进行比较.
- 协议指标 (积极,消极,总体百分比协议) 和Bland-Altman分析与类内相关性被用于比较.
主要成果:
- 与手动PSG评分相比,NBS算法在睡眠状态 (清醒,NREM,REM) 上达到了83%的总体一致性,CI患者的REM状态达到了92%的一致性.
- 对于OSA的严重程度,NBS对中度至重度OSA表现出100%的同意,对最小OSA表现出91%的同意.
- 与未得分的睡眠相比,NBS对呼吸暂停-呼吸暂停指数 (AHI) 和总睡眠时间 (TST) 的绝对误差明显较低,AHI和TST的类内相关性更高.
结论:
- 诺克斯BodySleepTM (NBS) AI算法提供了准确的睡眠状态估计和阻塞性睡眠呼吸暂停 (OSA) 严重程度评估.
- 与手动得分的多睡眠学 (PSG) 相比,NBS显示出良好的正负百分比一致性.
- NBS算法为睡眠分析提供了一个可行的,自动化的解决方案,有可能简化临床协议.
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