细粒度和轻量级的OSA检测:一种基于CRNN的模型,用于精确的时间定位睡眠中的呼吸事件音频
Mengyu Xu1,2, Yanru Li1,2,3, Demin Han1,2,3
1Department of Sleep Medical Center, Beijing Tongren Hospital, Capital Medical University, Beijing 100730, China.
Diagnostics (Basel, Switzerland)
|February 27, 2026
概括
一种新的基于音频的查工具通过精确地定位呼吸事件来准确检测阻塞性睡眠呼吸暂停 (OSA). 这种轻量级的框架为诊断OSA提供了一个可扩展的解决方案,提高了查的可访问性.
科学领域:
- 医疗技术 医疗技术 医学技术
- 人工智能的人工智能
- 呼吸系统医学 呼吸系统医学
背景情况:
- 阻塞性睡眠呼吸暂停 (OSA) 是一种普遍的疾病,由于有限的多睡眠学 (PSG) 资源,经常被诊断不足.
- 基于音频的查提供了可扩展性,但在精确的呼吸事件定位和呼吸暂停-呼吸暂停指数 (AHI) 估计方面存在困难.
研究的目的:
- 开发一种细粒度,轻量级的检测框架,用于使用非接触式音频信号进行OSA查.
- 为了实现精确的呼吸事件定位和准确的AHI估计,以改善OSA诊断.
主要方法:
- 提出了一个双流卷积循环神经网络 (CRNN),将Log Mel光谱和能量配置文件与BiLSTM集成.
- 该模型在PSG-Audio数据集上进行了训练,并在独立的外部队列上进行了验证,评估了框架级,事件级和患者级的表现.
主要成果:
- 该CRNN实现了精确的事件定位 (IoU为0.82) 和音频衍生和PSG衍生AHI (r = 0.96) 之间的强相关性.
- 该模型在外部验证中显示了各种AHI值的高灵敏度和特异性,表明了强大的选能力.
结论:
- 开发的双流CRNN为非接触性OSA查提供了强大的和临床上可解释的解决方案.
- 该框架显示了作为可访问的家庭OSA查的可靠工具的潜力,特别有效于轻度和重度病例.
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