摘要
低频振荡信号是血液动力学参数自发缓慢的变化信号,是反映局部的血液动力反应的重要标志。近红外光谱技术能够提供基于血红蛋白浓度变化的血液动力信息,从而反映大脑皮质的血氧代谢情况。然而作为检测低频振荡信号的重要手段,以往的近红外光谱设备在同步采集不同部位的数据时存在重大的缺陷,尤其是在血管末梢部位。由此,开发了一款基于近红外光谱技术的多通道血氧仪,可以同时检测不同部位(耳,手,脚)的低频振荡信号。对25个健康人静息状态下的低频振荡信号进行采集,然后对采集的数据进行时频分析和相关性分析,结果发现在人体对称位置的低频振荡信号存在较高的相关性,并且不同位置信号的时延在特定的范围之内,这项发现对于人体某些疾病的预防和治疗有重要的意义。
The low-frequency oscillation signal is a spontaneous and slow change signal of hemodynamic parameters,and is an important indicator reflecting the local hemodynamic response.Near-infrared spectroscopy provides hemodynamic information based on changes in hemoglobin concentration,reflecting blood oxygen metabolism in the cerebral cortex.However,as an important means of detecting low-frequency oscillating signals,the conventional near-infrared spectroscopy apparatus has significant defects in synchronously collecting data of different parts,especially at the end of blood vessels.Thus,a multi-channel oximeter based on near-infrared spectroscopy was developed to simultaneously detect low-frequency oscillating signals at different locations(ears,hands,feet).The low-frequency oscillating signals of 25 healthy people are collected at rest,and then the time-frequency analysis and correlation analysis of the collected data are carried out.It is found that the low-frequency oscillating signals in the symmetrical position of the human body have high correlation,the delay of the signal is within a certain range in different positions.This discovery is of great significance for the prevention and treatment of certain diseases in the human body.
作者
尹世敏
张海兵
南赛
于美玲
李英伟
YIN Shimin;ZHANG Haibing;NAN Sai;YU Meiling;LI Yingwei(Department of Neurology,PLA Rocket Force General Hospital,Beijing 100088,China;School of Information Science and Engineering,Yanshan University,Qinhuangdao,Hebei 066004,China)
出处
《燕山大学学报》
CAS
北大核心
2018年第5期416-421,共6页
Journal of Yanshan University
基金
国家自然科学基金资助项目(61827811)
河北省教育厅科学研究计划项目(ZD2016161)
关键词
低频振荡信号
近红外光谱
传播特性
时频分析
相关性分析
low-frequency oscillations
near-infrared spectrum
propagation characteristics
time-frequency analysis
correlation analysis