自主监督的语音表示用于睡眠呼吸暂停严重程度预测
IEEE transactions on bio-medical engineering
|January 20, 2026
概括
语音分析显示了查阻塞性睡眠呼吸暂停 (OSA) 的前景. 这种方法可以使用母音来评估OSA风险和严重程度,为传统诊断提供了经济有效的替代方案.
科学领域:
- 睡眠医学 睡眠医学
- 生物医学工程 生物医学工程
- 语音处理 语音处理
背景情况:
- 阻塞性睡眠呼吸暂停 (OSA) 很常见,但往往没有被诊断出来.
- 多人睡眠学 (PSG) 是黄金标准,但资源密集型.
- 语音特征与OSA有关,可能提供一种查替代方案.
研究的目的:
- 开发一个基于语音的管道用于OSA查和严重程度估计.
- 使用自主监督学习 (SSL) 和多模式声学功能.
- 为了克服以前基于语音的OSA研究仅关注声学特征的局限性.
主要方法:
- 一个新的融合框架,结合SSL衍生的语音表示和传统的声学特征.
- 对于OSA查和呼吸暂停-呼吸暂停指数 (AHI) 估计,对清醒期间的元音发音的分析.
- 从三个研究站点的不同参与者收集的数据.
主要成果:
- 在女性中,OSA查的平衡精度为0.79 (AHI ≥10) 和0.74 (AHI ≥30),在男性中为0.80和0.78.
- AHI估计显示女性平均绝对误差为12.0事件/小时 (r=0.63),男性平均误差为14.7事件/小时 (r=0.52).
- 这些模型显示了跨不同人口群体的概括性.
结论:
- 语音,特别是母音发音,是OSA风险和严重程度的可行的生物标志物.
- 这种基于语音的方法为OSA查提供了一种低负担,成本效益的方法.
- 这些发现对可扩展的睡眠健康评估有重大影响.
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