Suppr超能文献

光电容积脉搏波成像与 10 个波长手掌动脉搏动压力波分析。

Photoplethysmographic imaging and analysis of pulsatile pressure wave in palmar artery at 10 wavelengths.

机构信息

East Carolina Univ., United States.

Hunan Institute of Science and Technology, China.

出版信息

J Biomed Opt. 2022 Nov;27(11). doi: 10.1117/1.JBO.27.11.116004.

Abstract

SIGNIFICANCE

As a noncontact method, imaging photoplethysmography (iPPG) may provide a powerful tool to measure pulsatile pressure wave (PPW) in superficial arteries and extract biomarkers for monitoring of artery wall stiffness.

AIM

We intend to develop a approach for extraction of the very weak cardiac component from iPPG data by identifying locations of strong PPW signals with optimized illumination wavelength and determining pulse wave velocity (PWV).

APPROACH

Monochromatic in vivo iPPG datasets have been acquired from left hands to investigate various algorithms for retrieval of PPW signals, distribution maps and waveforms, and their dependence on arterial location and wavelength.

RESULTS

A robust algorithm of pixelated independent component analysis (pICA) has been developed and combined with spatiotemporal filtering to retrieve PPW signals. Spatial distributions of PPW signals have been mapped in 10 wavelength bands from 445 to 940 nm and waveforms were analyzed at multiple locations near the palmar artery tree. At the wavelength of 850 nm selected for timing analysis, we determined PWV values from 12 healthy volunteers in a range of 0.5 to 5.8 m/s across the hand region from wrist to midpalm and fingertip.

CONCLUSIONS

These results demonstrate the potentials of the iPPG method based on pICA algorithm for translation into a monitoring tool to characterize wall stiffness of superficial artery by rapid and noncontact measurement of PWV and other biomarkers within 10 s.

摘要

意义

作为一种非接触式方法,成像光体积描记法(iPPG)可以提供一种强大的工具来测量浅层动脉的脉动压力波(PPW)并提取用于监测动脉壁僵硬度的生物标志物。

目的

我们旨在通过识别具有优化照明波长的强 PPW 信号的位置并确定脉搏波速度(PWV),从 iPPG 数据中提取非常微弱的心脏分量的方法。

方法

从左手采集单色体内 iPPG 数据集,以研究各种用于检索 PPW 信号、分布图和波形的算法,以及它们对动脉位置和波长的依赖性。

结果

已经开发出一种强大的像素独立成分分析(pICA)算法,并将其与时空滤波相结合,以检索 PPW 信号。已经在 445 到 940nm 的 10 个波长带中映射了 PPW 信号的空间分布,并在手掌动脉树附近的多个位置分析了波形。在选择用于定时分析的 850nm 波长下,我们在 12 名健康志愿者中确定了 PWV 值,范围从 0.5 到 5.8m/s,横跨手腕到手掌中部和指尖的手部区域。

结论

这些结果表明,基于 pICA 算法的 iPPG 方法具有潜力,可以快速、非接触地测量 PWV 和其他生物标志物,在 10 秒内将其转化为监测工具,以表征浅层动脉的壁僵硬度。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/525b/9647835/07c63d29a08b/JBO-027-116004-g001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验