在急性压力期间心率变化 (HRV):对时间频率分析的三种方法进行比较
Bérangère Villatte1, Sayeed Devraj Kizuk2, Jean-Marc Lina3
1School of Speech Pathology and Audiology, Université de Montréal, Pavillon Parc, 7077 avenue du Parc, C.P. 6128, succ. Centre-Ville, Montreal, Quebec, H3T 1J4, CANADA.
Physiological measurement
|January 28, 2026
概括
使用连续波形变换 (CWT) 和集体实证模式分解 (EEMD) 的时间频率 (TF) 分析在压力期间有效地确定了动态心率变化 (HRV) 模式. 与传统技术相比,这些先进的方法为自主神经系统反应提供了更好的洞察力.
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
- 信号处理 信号处理
背景情况:
- 在压力下心脏信号是动态和复杂的.
- 传统的心率变化 (HRV) 分析方法通常假定信号静止或线性,这可能不准确地反映压力条件.
- 时间频率 (TF) 分析为分析动态生理信号提供了一个潜在的解决方案.
研究的目的:
- 在诱导应力期间应用和比较TF分析,以识别节拍到节拍RR时间序列数据中的振荡模式.
- 评估连续波形变换 (CWT) 和集体实证模式分解 (EEMD) 与HRV分析的标准短期里埃变换 (STFT) 的有效性.
- 研究在压力期间自主神经系统的激活,反应和适应能力.
主要方法:
- 从30名健康成年人的心电图中提取了节奏到节奏的RR时间序列数据.
- 参与者接受了三个压力任务:心理计算,噪音暴露和冷压器测试.
- 应用了连续波形变换 (CWT),集体实证模式分解 (EEMD) 和短期里埃变换 (STFT) 来使用30秒窗口分析分为预期,压力和恢复期的信号.
主要成果:
- 三种TF方法都检测到与基线相比,压力期间低频 (LF) 和高频 (HF) 频段的动态时间变化.
- 在压力期间,CWT和EMD在识别LF和HF差异方面表现出比STFT更高的灵敏度.
- CWT提供了卓越的时间和频率分辨率,特别是在低频段,揭示了标准频段之外的模式;EEMD的个人模式分析跟踪了瞬间的频率和振幅变化.
结论:
- CWT和EEMD是分析压力引起的HRV的宝贵工具,为动态生理反应提供了更深入的见解.
- 这些先进的TF方法提供了关于在压力期间自主神经系统的延迟,反应和适应能力的关键信息.
- 这些发现表明,我们应该转向更复杂的信号处理技术,以便全面了解压力下的心血管调节.
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