Cerebral Blood Flow Autoregulation Measurement via Bioimpedance Technology

Chen, Jia; Ke, Li; Du, Qiang; Zheng, Yi; Liu, Y

doi:10.1109/tim.2022.3169544

Cited by 8 publications

(4 citation statements)

References 21 publications

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“…The RF signal exhibits periodic pulsations with pronounced peaks and troughs. This signal includes a primary wave caused by blood vessel constriction and a secondary wave caused by blood vessel rebound ( Chen et al, 2022b ). The blue lines in Figures 5A–F present the changes in RF pulsations 30 min post-caffeine consumption, with observations recorded at five-minute interval, and the red lines show RF signals before caffeine consumption.…”

Section: Resultsmentioning

confidence: 99%

Dynamic cerebral blood flow assessment based on electromagnetic coupling sensing and image feature analysis

Gong,

Zeng,

Jiang

et al. 2024

Front. Bioeng. Biotechnol.

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Dynamic assessment of cerebral blood flow (CBF) is crucial for guiding personalized management and treatment strategies, and improving the prognosis of stroke. However, a safe, reliable, and effective method for dynamic CBF evaluation is currently lacking in clinical practice. In this study, we developed a CBF monitoring system utilizing electromagnetic coupling sensing (ECS). This system detects variations in brain conductivity and dielectric constant by identifying the resonant frequency (RF) in an equivalent circuit containing both magnetic induction and electrical coupling. We evaluated the performance of the system using a self-made physical model of blood vessel pulsation to test pulsatile CBF. Additionally, we recruited 29 healthy volunteers to monitor cerebral oxygen (CO), cerebral blood flow velocity (CBFV) data and RF data before and after caffeine consumption. We analyzed RF and CBFV trends during immediate responses to abnormal intracranial blood supply, induced by changes in vascular stiffness, and compared them with CO data. Furthermore, we explored a method of dynamically assessing the overall level of CBF by leveraging image feature analysis. Experimental testing substantiates that this system provides a detection range and depth enhanced by three to four times compared to conventional electromagnetic detection techniques, thereby comprehensively covering the principal intracranial blood supply areas. And the system effectively captures CBF responses under different intravascular pressure stimulations. In healthy volunteers, as cerebral vascular stiffness increases and CO decreases due to caffeine intake, the RF pulsation amplitude diminishes progressively. Upon extraction and selection of image features, widely used machine learning algorithms exhibit commendable performance in classifying overall CBF levels. These results highlight that our proposed methodology, predicated on ECS and image feature analysis, enables the capture of immediate responses of abnormal intracranial blood supply triggered by alterations in vascular stiffness. Moreover, it provides an accurate diagnosis of the overall CBF level under varying physiological conditions.

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Section: Resultsmentioning

confidence: 99%

Dynamic cerebral blood flow assessment based on electromagnetic coupling sensing and image feature analysis

Gong,

Zeng,

Jiang

et al. 2024

Front. Bioeng. Biotechnol.

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“…Brain BIOZ for Health Monitoring. Chen et al [28] investigated the correlation between brain BIOZ and cerebral blood flow (CBF), establishing a mathematical model that serves as a valuable tool for monitoring CBF based on brain BIOZ. In addition, several previous studies have explored the utilization of brain BIOZ for non-invasive ICP monitoring.…”

Section: A Related Workmentioning

confidence: 99%

“…If BP can be directly estimated by brain BIOZ, the number of sensors attached to the patients can be reduced, thus improving patients' comfort. However, existing research focuses on the utilization of PPG and wrist BIOZ for cuff-less BP estimation [14]- [20], or investigates the correlation between brain BIOZ and ICP [26]- [28]. Studies about the relationship between brain BIOZ and BP are missing, which is the motivation for this study.…”

Section: Introductionmentioning

confidence: 99%

Non-invasive cuff-less blood pressure estimation using a hybrid deep learning model

et al. 2021

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Conventional blood pressure (BP) measurement methods have different drawbacks such as being invasive, cuff-based or requiring manual operations. There is significant interest in the development of non-invasive, cuff-less and continual BP measurement based on physiological measurement. However, in these methods, extracting features from signals is challenging in the presence of noise or signal distortion. When using machine learning, errors in feature extraction result in errors in BP estimation, therefore, this study explores the use of raw signals as a direct input to a deep learning model. To enable comparison with the traditional machine learning models which use features from the photoplethysmogram and electrocardiogram, a hybrid deep learning model that utilises both raw signals and physical characteristics (age, height, weight and gender) is developed. This hybrid model performs best in terms of both diastolic BP (DBP) and systolic BP (SBP) with the mean absolute error being 3.23 ± 4.75 mmHg and 4.43 ± 6.09 mmHg respectively. DBP and SBP meet the Grade A and Grade B performance requirements of the British Hypertension Society respectively.

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“…It has been proved that transcranial bioimpedance monitoring, potentially a substitute for ICP measurement, also captures slow oscillations of cerebral intra-arterial volume stimulated by ABP changes [ 28 ]. The correlation coefficient between ABP and transcranial bioimpedance could also be used to assess the reactivity of cerebral vasculature and determine the lower limit of autoregulation [ 29 ].…”

Section: Introductionmentioning

confidence: 99%

Conductivity reactivity index for monitoring of cerebrovascular autoregulation in early cerebral ischemic rabbits

Jin

Zhuang

et al. 2023

BioMed Eng OnLine

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Background Cerebrovascular autoregulation (CVAR) is the mechanism that maintains constant cerebral blood flow by adjusting the caliber of the cerebral vessels. It is important to have an effective, contactless way to monitor and assess CVAR in patients with ischemia. Methods The adjustment of cerebral blood flow leads to changes in the conductivity of the whole brain. Here, whole-brain conductivity measured by the magnetic induction phase shift method is a valuable alternative to cerebral blood volume for non-contact assessment of CVAR. Therefore, we proposed the correlation coefficient between spontaneous slow oscillations in arterial blood pressure and the corresponding magnetic induction phase shift as a novel index called the conductivity reactivity index (CRx). In comparison with the intracranial pressure reactivity index (PRx), the feasibility of the conductivity reactivity index to assess CVAR in the early phase of cerebral ischemia has been preliminarily confirmed in animal experiments. Results There was a significant difference in the CRx between the cerebral ischemia group and the control group (p = 0.002). At the same time, there was a significant negative correlation between the CRx and the PRx (r = − 0.642, p = 0.002) after 40 min after ischemia. The Bland–Altman consistency analysis showed that the two indices were linearly related, with a minimal difference and high consistency in the early ischemic period. The sensitivity and specificity of CRx for cerebral ischemia identification were 75% and 20%, respectively, and the area under the ROC curve of CRx was 0.835 (SE = 0.084). Conclusion The animal experimental results preliminarily demonstrated that the CRx can be used to monitor CVAR and identify CVAR injury in early ischemic conditions. The CRx has the potential to be used for contactless, global, bedside, and real-time assessment of CVAR of patients with ischemic stroke.

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Cerebral Blood Flow Autoregulation Measurement via Bioimpedance Technology

Cited by 8 publications

References 21 publications

Dynamic cerebral blood flow assessment based on electromagnetic coupling sensing and image feature analysis

Dynamic cerebral blood flow assessment based on electromagnetic coupling sensing and image feature analysis

Non-invasive cuff-less blood pressure estimation using a hybrid deep learning model

Conductivity reactivity index for monitoring of cerebrovascular autoregulation in early cerebral ischemic rabbits

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