通过多式生理信号图像转换的深度学习方法来识别压力
Shiqi Yang1, Yuan Gao2, Yao Zhu1
1School of Biomedical Engineering, South-Central MINZU University, Wuhan, 430074, Hubei Province, China.
Scientific reports
|July 2, 2025
概括
这项研究引入了一种新的深度学习方法,用于使用生理信号准确检测心理压力. 该方法将信号转换为图像,在分类压力水平方面达到90%以上的准确性.
科学领域:
- 生物医学工程 生物医学工程
- 机器学习 机器学习
- 心理生理学 心理生理学
背景情况:
- 压力对健康有重大影响,需要准确的评估方法.
- 与传统方法相比,生理信号分析提供了客观的压力检测.
- 开发用于心理压力识别的先进技术对于幸福至关重要.
研究的目的:
- 提出一种新的深度学习方法来对心理压力状态进行分类.
- 为了利用多模体生理信号转化为RGB图像用于压力检测.
- 提高压力水平评估的准确性和客观性.
主要方法:
- 多模体生理信号被转换成RGB图像使用格拉米总和角场 (GASF),格拉米差异角场 (GADF) 和马尔科夫过渡场 (MTF) 的转换.
- 深度学习模型被用来分类心理压力状态 (压力,基线,娱乐).
- 模型的性能是根据准确性和F1得分进行评估的.
主要成果:
- 提出的深度学习模型实现了90.96%的高精度.
- 获得了91.67%的F1得分,表明了出色的分类性能.
- 在所有类别中,F1得分一直很高,证明了该车型的稳定性.
结论:
- 这种新的方法有效地捕捉了多式联络生理信号与精神压力之间的关系.
- 这种方法为精神压力识别提供了一个有希望和准确的工具.
- 深度学习与生理信号的图像转换相结合,增强了压力检测能力.
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