Suppr超能文献

使用多高斯模型化流速波形估计特征阻抗:虚拟受试者研究。

Estimation of Characteristic Impedance using Multi-Gaussian Modelled Flow Velocity Waveform: A Virtual Subjects Study.

出版信息

Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:2274-2277. doi: 10.1109/EMBC48229.2022.9871684.

Abstract

Characteristic impedance (Zc) of the blood vessel relates the pulsatile pressure to pulsatile blood flow velocity devoid of any wave reflections. Estimation of Z is useful for indirect evaluation of local pulse wave velocity and crucial for solving wave separation analysis (WSA) which separates the forward-backward pressure and flow velocity waveforms. As opposed to conventional WSA, which requires simultaneous measurement of pressure and flow velocity waveform, simplified WSA relies on modelled flow velocity waveforms, mainly introduced for the aorta. This work uses a multi-Gaussian decomposition (MGD) modelled flow velocity waveform to estimate Z by employing a frequency domain analysis, which is applicable to other arteries such as carotid. Thus obtained ZC is compared with Zc estimated from true flow velocity waveform for healthy (virtual) subjects taken for the carotid artery. The MGD modelled flow velocity waveform estimated ZC for a range of 4.98 to 34.79 with a group average of 16.43±0.10. The difference between the group average values of both ZC was only 4.72%. A statistically significant and strong correlation (r = 0.708, p < 0.0001) was observed for ZC obtained from MGD modelled flow velocity waveform with ZC obtained from actual flow velocity waveform. The bias for ZC1 between the two methods was 0.74, with confidence intervals (CIs) between 7.44 and -5.96 for the Bland-Altman analysis. Therefore, ZC from MGD modelled flow velocity waveform is a potential surrogate of the flow velocity model for WSA at the carotid artery. Clinical Relevance- This study provides a new method to derive characteristic impedance without the measurement of actual flow velocity waveform. The method requires a single pulse waveform (pressure or diameter).

摘要

血管的特征阻抗 (Zc) 使脉动压力与脉动血流速度相关,而不考虑任何波的反射。Z 的估计对于间接评估局部脉搏波速度非常有用,对于解决波分离分析 (WSA) 也至关重要,WSA 可分离正向和反向压力和流速波形。与需要同时测量压力和流速波形的传统 WSA 相反,简化的 WSA 依赖于模型化的流速波形,主要用于主动脉。本工作使用多高斯分解 (MGD) 模型化的流速波形,通过频域分析来估计 Z,该方法适用于其他动脉,如颈动脉。因此,获得的 ZC 与从健康(虚拟)受试者的颈动脉获取的真实流速波形估计的 Zc 进行比较。MGD 模型化的流速波形估计的 ZC 范围为 4.98 至 34.79,组平均为 16.43±0.10。两种 ZC 的组平均值之间的差异仅为 4.72%。从 MGD 模型化的流速波形获得的 ZC 与从实际流速波形获得的 ZC 之间观察到具有统计学意义的强相关性 (r = 0.708,p < 0.0001)。两种方法之间 ZC1 的偏差为 0.74,Bland-Altman 分析的置信区间 (CI) 为 7.44 至-5.96。因此,MGD 模型化的流速波形的 ZC 是颈动脉 WSA 流速模型的潜在替代物。临床意义-本研究提供了一种无需测量实际流速波形即可获得特征阻抗的新方法。该方法只需要一个脉冲波形(压力或直径)。

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验