PDF(Pubmed)
Abstract：
Wearable technologies face challenges due to signal instability, hindering their usage. Thus, it is crucial to comprehend the connection between dynamic patterns in photoplethysmography (PPG) signals and cardiovascular health. In our study, we collected 401 multimodal recordings from two public databases, evaluating hemodynamic conditions like blood pressure (BP), cardiac output (CO), vascular compliance (C), and peripheral resistance (R). Using irregular-resampling auto-spectral analysis (IRASA), we quantified chaotic components in PPG signals and employed different methods to measure the fractal dimension (FD) and entropy. Our findings revealed that in surgery patients, the power of chaotic components increased with vascular stiffness. As the intensity of CO fluctuations increased, there was a notable strengthening in the correlation between most complexity measures of PPG and these parameters. Interestingly, some conventional morphological features displayed a significant decrease in correlation, indicating a shift from a static to dynamic scenario. Healthy subjects exhibited a higher percentage of chaotic components, and the correlation between complexity measures and hemodynamics in this group tended to be more pronounced. Causal analysis showed that hemodynamic fluctuations are main influencers for FD changes, with observed feedback in most cases. In conclusion, understanding chaotic patterns in PPG signals is vital for assessing cardiovascular health, especially in individuals with unstable hemodynamics or during ambulatory testing. These insights can help overcome the challenges faced by wearable technologies and enhance their usage in real-world scenarios.
摘要：
由于信号不稳定，可穿戴技术面临挑战，阻碍他们的使用。因此，理解光电体积描记术(PPG)信号中的动态模式与心血管健康之间的联系至关重要.在我们的研究中,我们从两个公共数据库收集了401个多模式记录，评估血液动力学状况，如血压（BP），心输出量(CO),血管顺应性（C），和外围电阻（R）。使用不规则重采样自动光谱分析(IRASA)，我们量化了PPG信号中的混沌成分，并采用不同的方法来测量分形维数（FD）和熵。我们的研究结果表明，在手术患者中，混沌成分的力量随着血管硬度的增加而增加。随着CO波动强度的增加，PPG的大多数复杂性测量值与这些参数之间的相关性显着增强。有趣的是,一些传统的形态学特征显示出相关性显着下降，指示从静态场景到动态场景的转变。健康受试者表现出更高比例的混沌成分，在该组中，复杂性测量值与血液动力学之间的相关性趋于更加明显。因果分析显示,血流动力学波动是FD变化的主要影响因素,在大多数情况下观察到的反馈。总之,理解PPG信号中的混沌模式对于评估心血管健康至关重要，尤其是在血液动力学不稳定的个体或在非卧床测试期间。这些见解可以帮助克服可穿戴技术所面临的挑战，并增强其在现实世界场景中的使用。
