使用生理信号进行压力检测的多模式深度学习方法:整合时间和频率域特征
Jun-Zhi Xiang1, Qin-Yong Wang2, Zhi-Bin Fang3
1Emergency Department, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
Frontiers in physiology
|April 16, 2025
概括
这项研究引入了一种多式联网深度学习方法,用于使用可穿戴生理数据准确检测压力. 这种方法可以提高压力监测在高压的工作,如护理.
科学领域:
- 生物医学工程 生物医学工程
- 机器学习 机器学习
- 职业健康 职业健康 职业健康
背景情况:
- 在像护士这样的高压职业中,压力检测至关重要.
- 可穿戴设备为持续的生理数据收集提供了潜力.
- 现有的方法在现实世界中可能缺乏稳定性,间歇性数据场景.
研究的目的:
- 开发一种基于深度学习的多模式压力检测方法 (MMFD-SD).
- 为了利用间歇性收集的生理信号 (加速计,EDA,HR,皮肤温度).
- 为了在护士职业中验证该方法,作为代表性的高压职业.
主要方法:
- 一个多式联网深度学习框架,集成时间域和频域特征.
- 应用数据增强 (滑动窗口,动) 和合成少数人过量采样技术 (SMOTE).
- 定制卷积神经网络 (CNN) 架构用于特征处理和分类.
主要成果:
- 该MMFD-SD方法实现了91.00%的准确性和0.91 F1-score.
- 与传统的机器学习模型相比,在准确性和稳定性方面有显著的改进.
- 除研究证实了整合两个特征域的关键作用.
结论:
- 该MMFD-SD模型提供了一个准确和强大的解决方案,用于压力检测使用集成的生理特征.
- 该方法适用于间歇性数据收集的职业环境.
- 未来的工作包括探索实时检测和增强的模型通用化.
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