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预测睡眠呼吸暂停事件使用CNN转换器网络和无接触呼吸振动信号
Yuhang Chen1,2, Shuchen Yang3, Huan Li4,5
1School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China.
Bioengineering (Basel, Switzerland)
|July 29, 2023
概括
准确预测阻塞性睡眠呼吸暂停 (OSA) 事件对于开发新疗法至关重要. 使用呼吸振动的新型CNN变压器模型实现了85.9%的准确性,超过了用于更好的OSA管理的传统方法.
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) 影响全球超过4.25亿人.
- 准确预测OSA事件对于治疗开发至关重要,但仍然是一个研究缺口.
- 目前的预测方法需要进一步推进以改善临床应用.
研究的目的:
- 开发一种用于预测睡眠呼吸暂停事件的新框架.
- 为了利用低频呼吸振动来检测睡眠呼吸暂停.
- 使用先进的人工智能提高睡眠呼吸暂停事件预测的准确性和效率.
主要方法:
- 使用压电传感器捕获呼吸引起的振动.
- 开发了一个CNN变压器网络,从振动信号中提取本地和全球特征.
- 对105名受试者进行过夜记录,用于模型培训和验证.
主要成果:
- 在五倍交叉验证中获得了85.9%的准确性和85.8%的F1分数.
- 与经典模型相比,表现出3.5% (精度) 和5.3% (F1评分) 的性能改进.
- 在交叉验证中观察到2.3%和3.8%的改进,突出显示模型的稳定性.
结论:
- 拟议的CNN转换器模型有效地预测睡眠呼吸暂停事件.
- 这种基于振动的框架为改善OSA治疗和临床管理提供了一种新方法.
- 这项研究为非侵入性睡眠呼吸暂停检测和监测提供了一个有希望的方向.
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