使用无线雷达框架与深度学习方法相结合,在家庭环境中评估阻塞性睡眠呼吸暂停的严重程度
Kun-Ta Lee1, Wen-Te Liu2,3,4,5,6, Yi-Chih Lin3,7
1Respiratory Therapy Room, Division of Pulmonary Medicine, Taipei Medical University-Shuang Ho Hospital, New Taipei City, Taiwan.
Journal of multidisciplinary healthcare
|January 28, 2025
概括
这项研究引入了无线雷达系统与深度学习相结合,在家中选阻塞性睡眠呼吸暂停 (OSA). 雷达系统显示承诺作为传统睡眠呼吸暂停测试的无传感器替代方案.
科学领域:
- 生物医学工程 生物医学工程
- 睡眠医学 睡眠医学
- 人工智能的人工智能
背景情况:
- 阻塞性睡眠呼吸暂停 (OSA) 诊断通常依赖于多睡眠学 (PSG) 和家庭睡眠暂停测试 (HSAT).
- 目前的诊断方法需要安装传感器,这可能会扰乱睡眠并影响结果.
- 需要用于OSA风险评估的非侵入性,家庭查工具.
研究的目的:
- 评估一个与深度学习集成的无线雷达框架,用于基于家庭的OSA风险选.
- 评估使用雷达技术作为传统OSA诊断传感器的非接触替代品的可行性.
主要方法:
- 未来收集80名参与者在147个夜晚的家庭睡眠数据,使用HSAT和24GHz无线雷达系统.
- 开发混合深度神经决策树模型来分析雷达信号和识别呼吸事件.
- 从HSAT获得的呼吸暂停-低呼吸暂停指数 (AHI) 与来自雷达的呼吸障碍指数 (bRDITIB) 之间的相关性和一致性分析,包括使用Youden指数建立切断值.
主要成果:
- 在AHI和bRDITIB之间发现了强烈的相关性 (ρ = 0.87) 和高一致性 (93.88%).
- 基于雷达的模型在中度至重度的OSA中达到83.67%的准确性 (截止点:21.19事件/小时),在严重的OSA中达到93.21%的准确性 (截止点:28.14事件/小时).
- 使用已确定的值对多类OSA分类的平均准确率为78.23%.
结论:
- 无线雷达框架展示了在家庭OSA查中作为HSAT无接触,准确的替代品的潜力.
- 对于雷达衍生指数的确定的切断值为OSA风险评估提供了可接受的准确性.
- 需要进一步的研究来优化和验证基于雷达的总睡眠时间估计,以实现独立的临床应用.
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