Zeng Lingxi, Li Gen, Zhang Maoting, Zhu Rui, Chen Jingbo, Li Mingyan, Yin Shengtong, Bai Zelin, Zhuang Wei, Sun Jian
School of Pharmacy and Bioengineering, Chongqing University of Technology, Chongqing, China.
College of Biomedical Engineering, Army Medical University, Chongqing, China.
PeerJ. 2022 Feb 23;10:e13002. doi: 10.7717/peerj.13002. eCollection 2022.
Cerebral blood flow (CBF) monitoring is of great significance for treating and preventing strokes. However, there has not been a fully accepted method targeting continuous assessment in clinical practice. In this work, we built a noninvasive continuous assessment system for cerebral blood flow pulsation (CBFP) that is based on magnetic induction phase shift (MIPS) technology and designed a physical model of the middle cerebral artery (MCA). Physical experiments were carried out through different simulations of CBF states. Four healthy volunteers were enrolled to perform the MIPS and ECG synchronously monitoring trials. Then, the components of MIPS related to the blood supply level and CBFP were investigated by signal analysis in time and frequency domain, wavelet decomposition and band-pass filtering. The results show that the time-domain baseline of MIPS increases with blood supply level. A pulse signal was identified in the spectrum (0.2-2 Hz in 200-2,000 ml/h groups, respectively) of MIPS when the simulated blood flow rate was not zero. The pulsation frequency with different simulated blood flow rates is the same as the squeezing frequency of the feeding pump. Similar to pulse waves, the MIPS signals on four healthy volunteers all had periodic change trends with obvious peaks and valleys. Its frequency is close to that of the ECG signal and there is a certain time delay between them. These results indicate that the CBFP component can effectively be extracted from MIPS, through which different blood supply levels can be distinguished. This method has the potential to become a new solution for non-invasive and comprehensive monitoring of CBFP.
脑血流(CBF)监测对于中风的治疗和预防具有重要意义。然而,在临床实践中尚未有完全被接受的针对连续评估的方法。在这项工作中,我们基于磁感应相移(MIPS)技术构建了一种用于脑血流脉动(CBFP)的无创连续评估系统,并设计了大脑中动脉(MCA)的物理模型。通过对CBF状态的不同模拟进行物理实验。招募了四名健康志愿者同步进行MIPS和心电图监测试验。然后,通过时域和频域信号分析、小波分解和带通滤波研究了与供血水平和CBFP相关的MIPS成分。结果表明,MIPS的时域基线随供血水平增加。当模拟血流速度不为零时,在MIPS频谱中(分别在200 - 2000 ml/h组中为0.2 - 2 Hz)识别出脉冲信号。不同模拟血流速度下的脉动频率与输液泵的挤压频率相同。与脉搏波类似,四名健康志愿者的MIPS信号均具有明显的峰谷周期性变化趋势。其频率接近心电图信号频率,且两者之间存在一定的时间延迟。这些结果表明,可以从MIPS中有效提取CBFP成分,借此区分不同的供血水平。该方法有可能成为一种用于CBFP无创和全面监测的新解决方案。