A preliminary study on the application of electrical impedance tomography based on cerebral perfusion monitoring to intracranial pressure changes

Exploring Bio-Impedance Sensing for Intelligent Wearable Devices

The rapid growth of wearable technology has opened new possibilities for smart health-monitoring systems. Among various sensing methods, bio-impedance sensing has stood out as a powerful, non-invasive, and energy-efficient way to track physiological changes and gather important health information. This review looks at the basic principles behind bio-impedance sensing, how it is being built into wearable devices, and its use in healthcare and everyday wellness tracking. We examine recent progress in sensor design, signal processing, and machine learning, and show how these developments are making real-time health monitoring more effective. While bio-impedance systems offer many advantages, they also face challenges, particularly when it comes to making devices smaller, reducing power use, and improving the accuracy of collected data. One key issue is that analyzing bio-impedance signals often relies on complex digital signal processing, which can be both computationally heavy and energy-hungry. To address this, researchers are exploring the use of neuromorphic processors-hardware inspired by the way the human brain works. These processors use spiking neural networks (SNNs) and event-driven designs to process signals more efficiently, allowing bio-impedance sensors to pick up subtle physiological changes while using far less power. This not only extends battery life but also brings us closer to practical, long-lasting health-monitoring solutions. In this paper, we aim to connect recent engineering advances with real-world applications, highlighting how bio-impedance sensing could shape the next generation of intelligent wearable devices.

A novel method for detecting intracranial pressure changes by monitoring cerebral perfusion via electrical impedance tomography

BACKGROUND

Acute and critical neurological diseases are often accompanied with elevated intracranial pressure (ICP), leading to insufficient cerebral perfusion, which may cause severe secondary lesion. Existing ICP monitoring techniques often fail to effectively meet the demand for real-time noninvasive ICP monitoring and warning. This study aimed to explore the use of electrical impedance tomography (EIT) to provide real-time early warning of elevated ICP by observing cerebral perfusion.

METHODS

An intracranial hypertension model was prepared by injecting autologous un-anticoagulated blood into the brain parenchyma of twelve Landrace swine. Invasive ICP monitoring was used as a control method, and a high-precision EIT system was used to acquire and analyze the changing patterns of cerebral perfusion EIT image parameters with respect to ICP. Four EIT parameters related to cerebral perfusion were extracted from the images, and their potential application in detecting ICP elevation was analyzed.

RESULTS

When ICP increased, all EIT perfusion parameters decreased significantly (P < 0.05). When the subjects were in a state of intracranial hypertension (ICP > 22 mmHg), the correlation between EIT perfusion parameters and ICP was more significant (P < 0.01), with correlation coefficients ranging from -0.72 to -0.83. We tested the objects when they were in baseline ICP and in ICP of 15-40 mmHg. Under both circumstances, ROC curve analysis showed that the comprehensive model of perfusion parameters based on the random forest algorithm had a sensitivity and specificity of more than 90% and an area under the curve (AUC) of more than 0.9 for detecting ICP increments of both 5 and 10 mmHg.

CONCLUSION

This study demonstrates the feasibility of using perfusion EIT to detect ICP increases in real time, which may provide a new method for real-time non-invasive monitoring of patients with increased ICP.

Evaluation of cerebral perfusion heterogeneity by the electrical impedance tomography

Purpose

The purpose of this study was to evaluate the ability of global inhomogeneity index (GI) and left-right asymmetry index (AI) based on electrical impedance tomography (EIT) to be used in assessing cerebral perfusion heterogeneity. The diagnostic value of these two indices in identifying abnormalities in the degree of cerebral perfusion heterogeneity was also explored.

Methods

In this study, Transcranial Doppler (TCD) was used as a control, and unilateral carotid artery was compressed to change the degree of heterogeneity of cerebral perfusion in 15 healthy volunteers. The control group consisted of an additional 15 volunteers without any intervention. EIT perfusion images were obtained by calculating the impedance difference between at the beginning and end of cerebral vasodilation. Subsequently, GI and AI were calculated based on the pixel values of intracranial regions.

Results

The GI and AI values in the non-carotid artery compression (NCAC) group were significantly lower than those in the unilateral carotid artery compression (UCAC) group (P < 0.001), whereas there was no significant difference between the left carotid artery compression (LCAC) and right carotid artery compression (RCAC) groups. ROC analysis showed that the area under the curve (AUC), specificity and sensitivity of GI in distinguishing between NCAC and UCAC were 0.94, 0.90 and 0.87, respectively. The AUC, specificity and sensitivity of AI in distinguishing between NCAC and UCAC were 0.86, 0.87 and 0.73, respectively.

Conclusion

The results demonstrated that the GI and AI effectively quantify the distribution of intracranial perfusion, demonstrating excellent validity and interindividual comparability, and the ability to detect abnormal cerebral perfusion heterogeneity.