使用基于机器学习技术的心血管和呼吸系统数据对人类压力进行分类
Mahdis Yaghoubi1, Navid Adib1, Abolfazl Rezaei Monfared2
1Department of Engineering, School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran.
Journal of medical signals and sensors
|August 27, 2025
概括
这项研究显示,结合心脏和呼吸数据, 压力检测的准确性提高到95.6%. 这种多模式方法比单信号方法提供了更好的压力管理.
科学领域:
- 生理计算
- 生物医学信号处理
- 医疗保健中的机器学习
背景情况:
- 压力对心理健康,幸福感和表现有很大影响.
- 目前的压力检测方法通常依赖于单一的生理信号.
- 建议采用一种结合心血管和呼吸系统数据的新方法来检测压力.
研究的目的:
- 开发和验证用于准确实时压力检测的多模式系统.
- 调查用于压力分类的心血管和呼吸信号的有效性.
- 识别导致压力状态的关键生理特征.
主要方法:
- 在压力诱导任务中收集了15名参与者的心电图 (ECG) 和呼吸信号.
- 开发了对心电图信号处理的实时峰值检测算法.
- 从信号类型和使用的机器学习模型 (SVM,KNN,袋式树木,随机森林) 提取时间和频率域特征进行分类,并使用NASA-TLX评分进行验证.
主要成果:
- 与单一模式相比,多模式数据融合显著提高了应力分类的准确性,达到95.6%±1.7%.
- 前进特征选择从心血管和呼吸信号中确定了关键的区分特征.
- 开发的系统表现出优于单一模式方法的性能.
结论:
- 整合多模式生理数据,特别是心血管和呼吸信号,对于精确的压力检测是非常有效的.
- 这种方法超过单一模式方法和现有文献的性能.
- 这些发现支持实时监测系统的发展,
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