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压电陶传感器阵列基于阻塞性睡眠呼吸暂停事件检测.

Yi Liu, Zhengdong Li, Xiaomao Fan

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    |April 28, 2025
    PubMed
    概括
    此摘要是机器生成的。

    一个新的压电传感器阵列和AI方法 (DRFNet) 允许对阻塞性睡眠呼吸暂停 (OSA) 进行无接触监测. 该系统提高了检测准确度,为家庭睡眠研究提供了潜力.

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    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) 是一种严重的睡眠障碍,与心血管疾病和高血压等严重健康问题有关.
    • 无接触式压力传感器为睡眠监测提供了一种非侵入性的方法,但现有的方法受到噪音和位置限制的影响.
    • 来自有限的传感器的信号质量差,妨碍了准确的OSA事件检测.

    研究的目的:

    • 开发一种灵敏的传感器阵列,用于在床下捕获弱压信号.
    • 使用深度学习创建一种自动化方法来检测阻塞性睡眠呼吸暂停事件.
    • 评估开发系统的性能,在与成年志愿者的试点研究中进行评估.

    主要方法:

    • 设计了一个带有十六个传感器的压电陶传感器阵列 (PCSA),集成到一个用于胸部和腹部监测的床中.
    • 收集了36名成年志愿者的夜间压力信号和多睡眠学数据.
    • 开发了DRFNet,一种自动化的OSA事件检测方法,将ResNet18和DenseNet121融合在一起,用于时间域和频域特征提取.

    主要成果:

    • 在PCSA有效地捕获弱压信号通过床厚达30厘米.
    • 在OSA事件检测中,DRFNet实现了75.19%的灵敏度,87.78%的特异性和81.48%的准确性.
    • 与现有的最先进的方法相比,该系统表现出了竞争力的表现.

    结论:

    • PCSA和DRFNet的组合为非接触式睡眠监测提供了一个有前途的解决方案.
    • 这项技术有可能在嵌入式设备中部署,用于在家进行阻塞性睡眠呼吸暂停监测.
    • 开发的系统可以提高睡眠障碍诊断的可访问性和方便性.