van Bohemen Samuel J, Rogers Jeffrey M, Boughton Philip C, Clarke Jillian L, Valderrama Joaquin T, Kyme Andre Z
School of Biomedical Engineering, The University of Sydney, Sydney, NSW Australia.
Neurocare Group, Sydney, NSW Australia.
Biomed Eng Lett. 2023 Feb 9;13(2):185-195. doi: 10.1007/s13534-023-00265-z. eCollection 2023 May.
This paper describes a potential method to detect changes in cerebral blood flow (CBF) using electrocardiography (ECG) signals, measured across scalp electrodes with reference to the same signal across the chest-a metric we term the Electrocardiography Brain Perfusion index (EBPi). We investigated the feasibility of EBPi to monitor CBF changes in response to specific tasks. Twenty healthy volunteers wore a head-mounted device to monitor EBPi and electroencephalography (EEG) during tasks known to alter CBF. Transcranial Doppler (TCD) ultrasound measurements provided ground-truth estimates of CBF. Statistical analyses were applied to EBPi, TCD right middle cerebral artery blood flow velocity (rMCAv) and EEG relative Alpha (rAlpha) data to detect significant task-induced changes and correlations. Breath-holding and aerobic exercise induced highly significant increases in EBPi and TCD rMCAv ( < 0.01). Verbal fluency also increased both measures, however the increase was only significant for EBPi ( < 0.05). Hyperventilation induced a highly significant decrease in TCD rMCAv ( < 0.01) but EBPi was unchanged. Combining all tasks, EBPi exhibited a highly significant, weak positive correlation with TCD rMCAv (r = 0.27, < 0.01) and the Pearson coefficient between EBPi and rAlpha was r = - 0.09 ( = 0.05). EBPi appears to be responsive to dynamic changes in CBF and, can enable practical, continuous monitoring. CBF is a key parameter of brain health and function but is not easily measured in a practical, continuous, non-invasive fashion. EBPi may have important clinical implications in this context for stroke monitoring and management. Additional studies are required to support this claim.
The online version contains supplementary material available at 10.1007/s13534-023-00265-z.
本文描述了一种利用心电图（ECG）信号检测脑血流量（CBF）变化的潜在方法，该信号通过头皮电极测量，并参考胸部同一信号——我们将这一指标称为心电图脑灌注指数（EBPi）。我们研究了EBPi监测因特定任务引起的CBF变化的可行性。20名健康志愿者在进行已知会改变CBF的任务期间，佩戴头戴式设备来监测EBPi和脑电图（EEG）。经颅多普勒（TCD）超声测量提供了CBF的真实估计值。对EBPi、TCD右大脑中动脉血流速度（rMCAv）和EEG相对阿尔法（rAlpha）数据进行统计分析，以检测任务引起的显著变化和相关性。屏气和有氧运动导致EBPi和TCD rMCAv显著升高（<0.01）。言语流畅性也使这两项指标升高，但仅EBPi的升高具有显著性（<0.05）。过度换气导致TCD rMCAv显著降低（<0.01），但EBPi未发生变化。综合所有任务，EBPi与TCD rMCAv表现出高度显著的弱正相关（r = 0.27，<0.01），EBPi与rAlpha之间的皮尔逊系数为r = -0.09（= 0.05）。EBPi似乎对CBF的动态变化有反应，并且能够实现实际的连续监测。CBF是脑健康和功能的关键参数，但难以以实际、连续、非侵入性的方式进行测量。在这种情况下，EBPi可能对中风监测和管理具有重要的临床意义。需要更多研究来支持这一观点。
在线版本包含可在10.1007/s13534-023-00265-z获取的补充材料。
Zotero 插件
