使用连续可穿戴SPO信号检测和睡眠呼吸暂停的严重程度分类:多级特征方法
Nhung H Hoang1, Zilu Liang1,2
1Ubiquitous and Personal Computing Lab, Kyoto University of Advanced Science (KUAS), Kyoto 615-8577, Japan.
Sensors (Basel, Switzerland)
|April 28, 2025
概括
可穿戴的SpO2传感器可以使用多尺度分析来检测睡眠呼吸暂停的严重程度. 新的非线性特征提高了低分辨率数据的准确性,使得可访问的家庭查.
科学领域:
- 生物医学工程 生物医学工程
- 睡眠医学 睡眠医学
- 信号处理 信号处理
背景情况:
- 可穿戴设备为睡眠呼吸暂停检测提供了潜力,但由于信号分辨率有限而面临挑战.
- 准确的睡眠呼吸暂停诊断通常依赖于多睡眠学,这是昂贵和劳动密集的.
研究的目的:
- 调查使用可穿戴传感器的SpO2信号用于睡眠呼吸暂停检测和严重程度分类的可行性.
- 开发和评估用于低分辨率SpO2数据的多尺度特征工程方法.
主要方法:
- 提出了一种新的多尺度特征工程方法,可以从SpO2信号中提取跨时间尺度 (1s到600s) 的特征.
- 评估了传统和非线性SpO2特征与呼吸暂停-呼吸暂停指数 (AHI) 的相关性.
- 评估了多尺度特征提取对机器学习模型性能对睡眠呼吸暂停分类的影响.
主要成果:
- 传统的SpO2标志物 (例如,ODI,Lempel-Zip复杂性) 在较长的时间尺度上显示出与AHI的相关性减少.
- 非线性特征 (复杂,样本,模糊) 即使在粗略的时间尺度上也与AHI保持了强烈的相关性.
- 多尺度特征提取通过减少模型偏差,增强了机器学习模型性能,特别是贝叶斯和CatBoost.
结论:
- 多尺度特征工程对于分析睡眠呼吸暂停检测可穿戴设备的低分辨率SpO2数据是有效的.
- 这种方法可以提高廉价的家庭睡眠呼吸暂停查的可用性.
- 能够进行主动的睡眠健康监测,以早期发现睡眠问题.
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