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休息状态EEG功能连接性用于脑功能分析和阻塞性睡眠呼吸暂停的严重程度分类.

Minghui Liu, Ligang Zhou, Yalin Wang

    IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
    |September 9, 2025
    PubMed
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    阻塞性睡眠呼吸暂停 (OSA) 改变了大脑功能,静止状态EEG显示功能连接 (FC) 的变化与OSA严重程度相关. 这种非侵入性方法使用机器学习准确地分类OSA严重程度.

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    科学领域:

    • 神经科学是一个神经科学.
    • 睡眠医学 睡眠医学
    • 生物医学工程 生物医学工程

    背景情况:

    • 阻塞性睡眠呼吸暂停 (OSA) 是全球普遍存在的睡眠障碍,显著影响大脑功能.
    • 静止电脑图 (EEG) 为研究大脑活动提供了一种可行,经济有效和高时间分辨率的方法.
    • 了解OSA的神经效应对于开发有效的诊断和治疗策略至关重要.

    研究的目的:

    • 调查休息状态EEG功能连接 (FC) 与OSA严重性相关的变化.
    • 探索FC指标作为OSA严重程度分类的生物标志物的潜力.
    • 开发和验证机器学习模型,用于使用EEG衍生功能的OSA严重性预测.

    主要方法:

    • 一大群968名参与者接受了夜间多睡眠学 (PSG) 和15分钟的休息状态脑电图采集.
    • 参与者被分为健康对照组和轻度,中度和重度OSA组,基于呼吸暂停-呼吸暂停指数 (AHI).
    • 静态EEGFC使用相关性,连贯性,PLV和PLI进行计算,随后进行图形理论分析和机器学习模型的多变量特征选择.

    主要成果:

    • 观察到增加的FC与更高的OSA严重程度,表明神经补偿,以及特定大脑区域的区域性下降.
    • 图形理论分析表明,OSA患者的大脑网络中的中心性降低和拓重组.
    • 机器学习模型,特别是基于Corr的XGBoost模型,在使用选定的FC特征来分类OSA严重程度时,实现了高精度 (0.79) 和AUC (0.90).

    结论:

    • 静止状态EEGFC显示与OSA严重程度相关的大脑功能的显著改变.
    • 来自EEG的FC指标是OSA严重程度分类的有希望的非侵入性,无任务和可解释的工具.
    • 这种方法有助于准确评估OSA的严重程度,而不会干扰自然的睡眠模式.