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从空气流信号通过持久度曲线近似来确定儿童睡眠阶段
Shashank Manjunath1, Hau-Tieng Wu2, Aarti Sathyanarayana3
1Khoury College of Computer Sciences, Northeastern University, 440 Huntington Ave, Boston, 02115, MA, USA.
Computers in biology and medicine
|June 22, 2025
概括
持续曲线的新福里埃近似 (FAPC) 改进了仅使用空气流信号的自动睡眠分阶段,对儿科睡眠研究有希望.
科学领域:
- 生物医学工程 生物医学工程
- 数据科学数据科学数据科学
- 睡眠医学 睡眠医学
背景情况:
- 技术人员使用脑电图 (EEG) 和其他生物信号手动分阶段睡眠是具有挑战性的.
- 目前正在开发自动化算法,用于使用空气流信号而不是EEG进行睡眠分阶段.
- 现有的方法使用拓数据分析 (TDA) 与Hermite函数扩展的持久度曲线 (HEPC) 捕获空气流信号的部分信息.
研究的目的:
- 开发一种改进的睡眠分期方法,仅使用空气流信号.
- 为了证明这种方法对儿科患者的可行性.
- 作为一种新的特征化技术,引入持久度曲线 (FAPC) 的富里埃近似值.
主要方法:
- 使用拓数据分析 (TDA) 来进行信号特征化.
- 提出了对持久度曲线 (FAPC) 的富里埃近似方法,以增强从空气流信号获取信息.
- 在儿科睡眠研究中使用XGBoost模型进行睡眠阶段分类.
主要成果:
- FAPC方法提供了对现有HEPC方法的补充信息.
- 与基线方法相比,睡眠分期性能提高了4.9%.
- 在全国儿童医院睡眠数据库 (NCHSDB) 的1155个儿科睡眠研究中证明了FAPC技术的有效性.
结论:
- 建议使用FAPC的TDA特色化技术改善了从呼吸信号的睡眠阶段分类.
- 这种方法显示出在儿科睡眠研究中应用的重大前景.
- 基于TDA的特征化方法有可能在时间序列信号处理中得到更广泛的应用.
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