通过心率变量特征选择优化心理压力检测
Mohsen Behradfar1, Shotabdi Roy1, Joseph Nuamah1
1School of Industrial Engineering and Management, Oklahoma State University, Stillwater, OK 74078, USA.
Sensors (Basel, Switzerland)
|July 12, 2025
概括
心率变化 (HRV) 的特征可以检测心理压力. 特定的HRV指标,如正常化的低频电源,对压力监测系统有很大的希望.
科学领域:
- 身体生理学 身体生理学
- 生物医学工程 生物医学工程
- 数据科学数据科学数据科学
背景情况:
- 与压力相关的疾病越来越普遍,需要有效的检测方法.
- 心率变化 (HRV) 分析为生理压力提供了潜在的非侵入性生物标志物.
- 现有的压力检测方法可能缺乏效率或准确性.
研究的目的:
- 调查HRV特征在检测心理压力的有效性.
- 为了确定压力分类中最具歧视性的HRV特征.
- 使用HRV数据开发和验证一个强大的压力检测模型.
主要方法:
- 从心电图 (ECG) 信号中提取了93个HRV特征.
- 采用基于过器,包装和嵌入式功能选择策略.
- 使用递归特征消除与嵌套离开-一个主体-输出交叉验证来进行特征选择和模型优化.
主要成果:
- 在识别压力状态方面获得了0.76的F1高峰分数.
- 确定了两个关键的HRV特征:RR间隔的正常化中位数绝对偏差和正常化的低频功率.
- 在未见的数据集上验证了模型,得到了平均F1分数0.63.
结论:
- 针对性特征选择对于优化应力检测模型至关重要,尤其是在高维 HRV 数据方面.
- 具体的HRV指标表明,对精确有效地检测心理压力有很大的潜力.
- 这项研究有助于开发先进的压力监测系统,以改善心理健康评估.
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