A novel method for detecting intracranial pressure changes by monitoring cerebral perfusion via electrical impedance tomography
Neurological acute diseases, such as trauma, stroke, and brain tumors, are often accompanied by elevated intracranial pressure (ICP) . Persistently elevated ICP may reduce cerebral perfusion, leading to ischemia and hypoxia of brain tissues, which can ultimately lead to severe neurological damage and brain death, if left untreated . Early monitoring of elevated ICP is critical for the proper treatment of patients in neurocritical care and for the  improvement of prognosis . Therefore, real-time monitoring of elevated ICP to prevent inadequate cerebral perfusion is of great clinical value in the diagnosis and treatment of craniocerebral diseases. Existing ICP monitoring methods are mainly categorized into invasive ICP monitoring and noninvasive ICP monitoring . Invasive ICP monitoring is widely recognized as the gold standard for ICP monitoring. However, this method may lead to complications such as intracranial hemorrhage and infection during placement of the pressure transducer . Therefore, a variety of noninvasive techniques for ICP monitoring such as Transcranial Doppler , Optic Nerve Sheath Diameter , and Magnetic Resonance Imaging  have been developed, to indirectly obtain ICP information. However, these methods require specialized personnel in clinical applications. Also, they are not handy to use, and are not suitable for prolonged noninvasive real-time monitoring. As a result, there is an urgent clinical need for a noninvasive and harmless bedside ICP monitoring method with results that are easy to interpret, as well as realizing the monitoring and warning of ICP changes effectively. Electrical impedance tomography (EIT) is a non-invasive functional imaging technique that measures boundary voltages on body surfaces to estimate the spatial distribution of tissue resistivity in vivo . Different tissues have different impedance levels, which can change under physiological and pathological conditions. EIT can therefore detect physiological and pathological changes in internal impedance . EIT has been investigated in a number of scenarios, such as imaging of lung respiratory function and perfusion , functional brain imaging , and functional imaging of abdominal organs . Yang et al. investigated the correlation between EIT imaging results and ICP changes during mannitol dehydration, which showed that the changes in EIT reconstruction values caused by the dehydration effect are strongly correlated with the changes in ICP of patients . Yang et al.’s study demonstrated the unique advantages of the EIT for ICP monitoring, which, as a completely noninvasive and harmless monitoring approach, achieve long-term monitoring of intracranial conditions with high temporal resolution. It provided a basis for the application of EIT to noninvasive ICP monitoring. However, it was limited to only one case of mannitol dehydration therapy and focused on the basal impedance properties of brain tissue. Besides, the results might be affected by the nature of the brain disease, injury, or changes in physiological status, possibly damaging the specificity of the results. Continuously elevated ICP may disrupt the cerebral autoregulation, causing a decrease in cerebral blood flow (CBF), which in turn may lead to insufficient blood supply to brain tissues, resulting in poor perfusion . This phenomenon suggests that we can indirectly assess ICP changes by monitoring the state of cerebral perfusion , thus realizing real-time warning of the adverse consequences of elevated ICP. Given that blood is a highly conductive substance, alterations in its volume can have a considerable impact on the impedance value. It can be theoretically demonstrated that it is feasible to reconstruct the image of blood volume changes caused by cerebral blood flow pulsations through EIT. Therefore, our team carried out a series of exploratory research work on cerebral perfusion imaging around this mechanism in the early stage [ – ]. We successfully extracted the dynamic cerebral perfusion signal, and verified the feasibility of using EIT to characterize the perfusion process. These results provide a basis for EIT to monitor the cerebral perfusion status reflecting ICP changes. In this study, we constructed an intracranial hypertension swine model and used invasive ICP monitoring as a control method to analyze the changing pattern of cerebral perfusion parameters of EIT with ICP. We aimed to ascertain the feasibility of EIT in reflecting the ICP changes through the monitoring of cerebral perfusion. 

Animal preparation Twelve healthy Landrace swine weighing 18.5 ± 3 kg were used for the experiment. The experimental protocol and procedures were approved by the Animal Ethics Committee of the Air Force Medical University (Ethical permission number: IACUC-20241299). Anesthesia was induced in all swine with a dose of 3 mg/kg of Zoletil ®50, and the swine were extubated and anesthesia was maintained with isoflurane of concentration of 1–3%. The femoral artery was punctured with an indwelling needle to establish access for blood sampling. After arterial blood pressure, oxygen saturation, and end-expiratory carbon dioxide were monitored, the scalp of each Landrace swine was incised longitudinally, the skull was separated and exposed, the periosteum was scraped, and a window with a sagittal length of approximately 60 mm and a coronal length of approximately 60 mm was formed. Two holes were drilled using cranial drills, one at 15 mm to the right of the sagittal suture, 15 anterior to the coronal suture for the placement of ICP probe and the other at 15 mm on the right side of the sagittal suture, 10 mm on the posterior side of the coronal suture for the placement of a 22 G indwelling needle, which was inserted into the hole to a depth of 18 mm. In addition, 16 sterilized 1.2-mm dental root canal stumps (Xihu Biomaterials, Hangzhou, Zhejiang Province, China, square-head type) were drilled into the margins of the skull at equal intervals (Fig. A), none of which penetrated the dura mater. The above cranial holes were sealed with bone wax to minimize the possibility of cerebrospinal fluid leakage. ICP monitor and EIT system ICP data was acquired by an ICP monitor (SOPHYSA Inc., Orsay, France) with a frame rate of 2 frames per second (Fig. B). EIT data was obtained using EC-100 PRO (UTRON Technology Co., Ltd., Hangzhou, China, Fig. B), a high-speed and high-precision system developed by our team . The system operates over at a frequency range of 10–250 kHz, an output current range of 10–1250 μA, and a signal-to-noise ratio greater than 90 dB. For the experiments, the frame rate of data acquisition was set to 40 frames per second and the excitation frequency was set to50kHz. Experimental protocols The intracranial hypertension model was prepared by injecting autologous un-anticoagulated blood into the brain parenchyma of swine in batches. After connecting the electrodes to the EIT system, baseline EIT data was collected and ICP data acquisition was started simultaneously. After approximately 10 min, 2 ml of blood was withdrawn through an indwelling needle preplaced in the femoral artery and then injected into the parenchymal region of the brain through an indwelling needle preplaced intracranially at a uniform rate within 1 min. This process was repeated after approximately 10 min for a total of 5 times, and its time sequence is illustrated in Fig. C. Blood pressure was maintained constant throughout the procedure using norepinephrine. When the surgery was completed, the animals were put to death under deep anesthesia, the cranium was opened with a cranial drill and a bone-biting forceps, and the brain tissue was removed intact for observation of the location of the hematoma. EIT image reconstruction and perfusion parameter extraction The original boundary voltage signals collected by the EIT system were first passed through a bandpass filter with a passband frequency of 1–5 Hz to extract the voltage signals reflecting the dynamic blood perfusion. Then the circular finite element model conforming to the actual electrode distribution location was used for the forward problem calculation to obtain the potential at each node. Finally, the inverse problem calculation was carried out by the damped least squares method  to obtain the reconstructed image reflecting the dynamic changes of blood flow, as shown in Eq. ( ). 1 \documentclass[12pt]{minimal}
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				\begin{document}$$\Delta \rho = \left( {J^{T} J + \lambda R} \right)^{ - 1} J^{T} \Delta V$$\end{document} Δ ρ = J T J + λ R - 1 J T Δ V Δρ is the change in resistivity distribution between the current frame and the reference frame, ΔV is the normalized boundary voltage change between the current frame and the reference frame, J is the Jacobi matrix, λ is the regularization parameter, and R is the regularization matrix. The average reconstruction value (ARV) associated with the whole brain in the EIT image sequence was calculated by Eq. ( ). 2 \documentclass[12pt]{minimal}
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				\begin{document}$$ARV = \frac{{\left( {\mathop \sum \nolimits_{i = 1}^{N} \Delta \rho_{i} } \right)}}{N}$$\end{document} A R V = ∑ i = 1 N Δ ρ i N Δρ i denotes the reconstruction resistivity of the i-th pixel in the EIT image; N denotes the total number of pixels in the region of interest. ARV reflects dynamic changes in resistivity caused by pulsatile changes in the congested state of brain tissue. Since there is a significant difference in resistivity between blood and brain tissue, when the heart contracts, blood is pumped into the cranial cavity, resulting in a decrease in resistivity. This process is represented in the curve (Fig. D) as a drop from the peak (ARV p ) to the trough (ARV v ), termed the descending branch, with the corresponding time interval denoted as T d . Subsequently, during cardiac diastole, the blood gradually flows out of the cranial cavity, with a consequent rise in resistivity from ARV v back to the next peak (ARV p ′) defined as the ascending branch, with a time interval denoted as T a . A complete cerebral perfusion cycle is defined as a process that begins at ARV p , passes through ARV v , and reaches ARV p ′. In order to provide a more detailed description of the perfusion state, a series of parameters were extracted for each vascular pulse cycle. These included the mean perfusion velocity (MV), the systolic wave height (H s ), the inflow volume velocity (IV), and the angle between the ascending branch and the baseline (A ab ). The MV is used to quantify the rate of blood volume change in the vessels. It was calculated by Eq. ( ): 3 \documentclass[12pt]{minimal}
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				\begin{document}$$MV= \frac{\sum_{i=1}^{N}\left({ARV}_{p}-{ARV}_{i}\right)}{{T}_{a}+{T}_{d}}$$\end{document} M V = ∑ i = 1 N ARV p - ARV i T a + T d ARV i denotes the average reconstruction value of the i th EIT image frame; N denotes the total number of EIT image frames acquired in the current perfusion cycle. The H s primarily reflects the filling degree of the cerebral vessels, which is indicative of the degree of pulsatile blood flow supply. The IV is defined as the ratio of Hs to Ta, which serves to reflect the correlation between the two variables. The H s and IV were calculated by Eqs. ( ) and ( ), respectively: 4 \documentclass[12pt]{minimal}
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				\begin{document}$${H}_{s}={ARV}_{p}{-ARV}_{v}$$\end{document} H s = ARV p - A R V v 5 \documentclass[12pt]{minimal}
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				\begin{document}$$IV= \frac{{H}_{s}}{{T}_{d}}$$\end{document} I V = H s T d The A db is a parameter that reflects the elasticity of the cerebral blood vessels. It is calculated using Eq. ( ): 6 \documentclass[12pt]{minimal}
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				\begin{document}$${A}_{db}=arccos\left(\frac{{X}_{p}\left({X}_{v}-{X}_{p}\right)}{\sqrt{{{X}_{p}}^{2}}\bullet \sqrt{{\left({X}_{v}-{X}_{p}\right)}^{2}+{\left({Y}_{v}-{Y}_{p}\right)}^{2}}}\right)$$\end{document} A db = a r c c o s X p X v - X p X p 2 ∙ X v - X p 2 + Y v - Y p 2 The baseline is defined as the vector starting from \documentclass[12pt]{minimal}
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				\begin{document}$${\text{ARV}}_{\text{p}}$$\end{document} ARV p whose direction is parallel to the x-axis. \documentclass[12pt]{minimal}
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				\begin{document}$${\text{X}}_{\text{p}}{,\text{Y}}_{\text{p}}$$\end{document} X p , Y p are the x and y coordinates of \documentclass[12pt]{minimal}
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				\begin{document}$${\text{ARV}}_{\text{p}}$$\end{document} ARV p ; \documentclass[12pt]{minimal}
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				\begin{document}$${\text{X}}_{\text{v}},{\text{Y}}_{\text{v}}$$\end{document} X v , Y v are the x and y coordinates of \documentclass[12pt]{minimal}
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				\begin{document}$${\text{ARV}}_{\text{v}}$$\end{document} ARV v . Data analysis and statistical methods We analyzed our experiment results using SPSS 27.0 statistical software (SPSS Inc, Chicago, IL, USA) and MATLAB (MathWorks, R2022a, USA.). The level of significance for statistical analysis was 0.05. Data was tested for normality using the Shapiro–Wilk test. The ICP values were categorized into the following four groups according to the ICP grading criteria: 0–15, 15–20, 20–40, and >40 mmHg. To examine the mean perfusion parameters across samples at varying ICP levels, Friedman test was employed to investigate the variability of these parameters with ICP. Subsequently, the Bonferroni’s post-hoc test was utilized to conduct pairwise comparisons. PASS 15.0 was used to calculate the average statistical power of the four parameters based on the current sample size. The pixel values of functional EIT (fEIT) are not mere impedance changes, but valuable functional information that influences clinical decisions. A sequence of EIT images was calculated for each set of 5 perfusion cycles, from which the waveform of each pixel was extracted to calculate the average perfusion parameters, and these perfusion parameters were presented as images to generate fEIT images. Based on the International Traumatic Brain Injury Foundation Intervention criteria for the treatment of intracranial hypertension , we calculated the Spearman correlation coefficients between the perfusion parameters and ICP in the two intervals defined by an ICP value of 22 mmHg, and then assessed the differences in the correlation coefficients between the two intervals using a paired t-test. To assess the diagnostic efficacy of EIT parameters in detecting ICP elevation, the mean MV, H s , IV, and A db at baseline were first determined for each sample as reference values, and the normalized change in these parameters relative to baseline (ΔMV, ΔH s , ΔIV, ΔA db ), as well as the change in ICP (ΔICP), were calculated. With 500 randomly selected sample data per animal and a total of 6000 sample data for ROC curve analysis, we evaluated the efficacy of each independent EIT parameter change in detecting ICP elevations >5 and 10 mmHg. Finally, AUC, sensitivity and specificity were calculated from the ROC curves. Finally, we also modelled the ICP elevation change detection using the random forest algorithm. The study dataset was composed of 6000 samples of ΔMV, ΔH s , ΔIV, ΔA db , and Y. ΔMV, ΔH s , ΔIV, and ΔA db are the normalized rate of change of MV, H s , IV, and A db , respectively, with respect to the reference value. Y = {0,1} is the label (where 0 and 1 each indicate that the ΔICP is less than and greater than a certain threshold value). The dataset was then randomly divided into training (70%) and test (30%) datasets. The training set was utilized to train the random forest model. With the help of the test set, we comprehensively evaluated the detection efficiency of the model by calculating the AUC, sensitivity and specificity. The reference values chosen were normal ICP values and intracranial hypertension values (between 15 and 40 mmHg) because patients are often already in a state of intracranial hypertension at the time of initial monitoring, making it critical to explore the ability of the random forest algorithm to detect elevated ICP for different reference values. 

Twelve healthy Landrace swine weighing 18.5 ± 3 kg were used for the experiment. The experimental protocol and procedures were approved by the Animal Ethics Committee of the Air Force Medical University (Ethical permission number: IACUC-20241299). Anesthesia was induced in all swine with a dose of 3 mg/kg of Zoletil ®50, and the swine were extubated and anesthesia was maintained with isoflurane of concentration of 1–3%. The femoral artery was punctured with an indwelling needle to establish access for blood sampling. After arterial blood pressure, oxygen saturation, and end-expiratory carbon dioxide were monitored, the scalp of each Landrace swine was incised longitudinally, the skull was separated and exposed, the periosteum was scraped, and a window with a sagittal length of approximately 60 mm and a coronal length of approximately 60 mm was formed. Two holes were drilled using cranial drills, one at 15 mm to the right of the sagittal suture, 15 anterior to the coronal suture for the placement of ICP probe and the other at 15 mm on the right side of the sagittal suture, 10 mm on the posterior side of the coronal suture for the placement of a 22 G indwelling needle, which was inserted into the hole to a depth of 18 mm. In addition, 16 sterilized 1.2-mm dental root canal stumps (Xihu Biomaterials, Hangzhou, Zhejiang Province, China, square-head type) were drilled into the margins of the skull at equal intervals (Fig. A), none of which penetrated the dura mater. The above cranial holes were sealed with bone wax to minimize the possibility of cerebrospinal fluid leakage. 

ICP data was acquired by an ICP monitor (SOPHYSA Inc., Orsay, France) with a frame rate of 2 frames per second (Fig. B). EIT data was obtained using EC-100 PRO (UTRON Technology Co., Ltd., Hangzhou, China, Fig. B), a high-speed and high-precision system developed by our team . The system operates over at a frequency range of 10–250 kHz, an output current range of 10–1250 μA, and a signal-to-noise ratio greater than 90 dB. For the experiments, the frame rate of data acquisition was set to 40 frames per second and the excitation frequency was set to50kHz. 

The intracranial hypertension model was prepared by injecting autologous un-anticoagulated blood into the brain parenchyma of swine in batches. After connecting the electrodes to the EIT system, baseline EIT data was collected and ICP data acquisition was started simultaneously. After approximately 10 min, 2 ml of blood was withdrawn through an indwelling needle preplaced in the femoral artery and then injected into the parenchymal region of the brain through an indwelling needle preplaced intracranially at a uniform rate within 1 min. This process was repeated after approximately 10 min for a total of 5 times, and its time sequence is illustrated in Fig. C. Blood pressure was maintained constant throughout the procedure using norepinephrine. When the surgery was completed, the animals were put to death under deep anesthesia, the cranium was opened with a cranial drill and a bone-biting forceps, and the brain tissue was removed intact for observation of the location of the hematoma. 

The original boundary voltage signals collected by the EIT system were first passed through a bandpass filter with a passband frequency of 1–5 Hz to extract the voltage signals reflecting the dynamic blood perfusion. Then the circular finite element model conforming to the actual electrode distribution location was used for the forward problem calculation to obtain the potential at each node. Finally, the inverse problem calculation was carried out by the damped least squares method  to obtain the reconstructed image reflecting the dynamic changes of blood flow, as shown in Eq. ( ). 1 \documentclass[12pt]{minimal}
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				\begin{document}$$\Delta \rho = \left( {J^{T} J + \lambda R} \right)^{ - 1} J^{T} \Delta V$$\end{document} Δ ρ = J T J + λ R - 1 J T Δ V Δρ is the change in resistivity distribution between the current frame and the reference frame, ΔV is the normalized boundary voltage change between the current frame and the reference frame, J is the Jacobi matrix, λ is the regularization parameter, and R is the regularization matrix. The average reconstruction value (ARV) associated with the whole brain in the EIT image sequence was calculated by Eq. ( ). 2 \documentclass[12pt]{minimal}
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				\begin{document}$$ARV = \frac{{\left( {\mathop \sum \nolimits_{i = 1}^{N} \Delta \rho_{i} } \right)}}{N}$$\end{document} A R V = ∑ i = 1 N Δ ρ i N Δρ i denotes the reconstruction resistivity of the i-th pixel in the EIT image; N denotes the total number of pixels in the region of interest. ARV reflects dynamic changes in resistivity caused by pulsatile changes in the congested state of brain tissue. Since there is a significant difference in resistivity between blood and brain tissue, when the heart contracts, blood is pumped into the cranial cavity, resulting in a decrease in resistivity. This process is represented in the curve (Fig. D) as a drop from the peak (ARV p ) to the trough (ARV v ), termed the descending branch, with the corresponding time interval denoted as T d . Subsequently, during cardiac diastole, the blood gradually flows out of the cranial cavity, with a consequent rise in resistivity from ARV v back to the next peak (ARV p ′) defined as the ascending branch, with a time interval denoted as T a . A complete cerebral perfusion cycle is defined as a process that begins at ARV p , passes through ARV v , and reaches ARV p ′. In order to provide a more detailed description of the perfusion state, a series of parameters were extracted for each vascular pulse cycle. These included the mean perfusion velocity (MV), the systolic wave height (H s ), the inflow volume velocity (IV), and the angle between the ascending branch and the baseline (A ab ). The MV is used to quantify the rate of blood volume change in the vessels. It was calculated by Eq. ( ): 3 \documentclass[12pt]{minimal}
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				\begin{document}$$MV= \frac{\sum_{i=1}^{N}\left({ARV}_{p}-{ARV}_{i}\right)}{{T}_{a}+{T}_{d}}$$\end{document} M V = ∑ i = 1 N ARV p - ARV i T a + T d ARV i denotes the average reconstruction value of the i th EIT image frame; N denotes the total number of EIT image frames acquired in the current perfusion cycle. The H s primarily reflects the filling degree of the cerebral vessels, which is indicative of the degree of pulsatile blood flow supply. The IV is defined as the ratio of Hs to Ta, which serves to reflect the correlation between the two variables. The H s and IV were calculated by Eqs. ( ) and ( ), respectively: 4 \documentclass[12pt]{minimal}
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				\begin{document}$${H}_{s}={ARV}_{p}{-ARV}_{v}$$\end{document} H s = ARV p - A R V v 5 \documentclass[12pt]{minimal}
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				\begin{document}$$IV= \frac{{H}_{s}}{{T}_{d}}$$\end{document} I V = H s T d The A db is a parameter that reflects the elasticity of the cerebral blood vessels. It is calculated using Eq. ( ): 6 \documentclass[12pt]{minimal}
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				\begin{document}$${A}_{db}=arccos\left(\frac{{X}_{p}\left({X}_{v}-{X}_{p}\right)}{\sqrt{{{X}_{p}}^{2}}\bullet \sqrt{{\left({X}_{v}-{X}_{p}\right)}^{2}+{\left({Y}_{v}-{Y}_{p}\right)}^{2}}}\right)$$\end{document} A db = a r c c o s X p X v - X p X p 2 ∙ X v - X p 2 + Y v - Y p 2 The baseline is defined as the vector starting from \documentclass[12pt]{minimal}
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				\begin{document}$${\text{ARV}}_{\text{p}}$$\end{document} ARV p whose direction is parallel to the x-axis. \documentclass[12pt]{minimal}
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				\begin{document}$${\text{ARV}}_{\text{v}}$$\end{document} ARV v . 

We analyzed our experiment results using SPSS 27.0 statistical software (SPSS Inc, Chicago, IL, USA) and MATLAB (MathWorks, R2022a, USA.). The level of significance for statistical analysis was 0.05. Data was tested for normality using the Shapiro–Wilk test. The ICP values were categorized into the following four groups according to the ICP grading criteria: 0–15, 15–20, 20–40, and >40 mmHg. To examine the mean perfusion parameters across samples at varying ICP levels, Friedman test was employed to investigate the variability of these parameters with ICP. Subsequently, the Bonferroni’s post-hoc test was utilized to conduct pairwise comparisons. PASS 15.0 was used to calculate the average statistical power of the four parameters based on the current sample size. The pixel values of functional EIT (fEIT) are not mere impedance changes, but valuable functional information that influences clinical decisions. A sequence of EIT images was calculated for each set of 5 perfusion cycles, from which the waveform of each pixel was extracted to calculate the average perfusion parameters, and these perfusion parameters were presented as images to generate fEIT images. Based on the International Traumatic Brain Injury Foundation Intervention criteria for the treatment of intracranial hypertension , we calculated the Spearman correlation coefficients between the perfusion parameters and ICP in the two intervals defined by an ICP value of 22 mmHg, and then assessed the differences in the correlation coefficients between the two intervals using a paired t-test. To assess the diagnostic efficacy of EIT parameters in detecting ICP elevation, the mean MV, H s , IV, and A db at baseline were first determined for each sample as reference values, and the normalized change in these parameters relative to baseline (ΔMV, ΔH s , ΔIV, ΔA db ), as well as the change in ICP (ΔICP), were calculated. With 500 randomly selected sample data per animal and a total of 6000 sample data for ROC curve analysis, we evaluated the efficacy of each independent EIT parameter change in detecting ICP elevations >5 and 10 mmHg. Finally, AUC, sensitivity and specificity were calculated from the ROC curves. Finally, we also modelled the ICP elevation change detection using the random forest algorithm. The study dataset was composed of 6000 samples of ΔMV, ΔH s , ΔIV, ΔA db , and Y. ΔMV, ΔH s , ΔIV, and ΔA db are the normalized rate of change of MV, H s , IV, and A db , respectively, with respect to the reference value. Y = {0,1} is the label (where 0 and 1 each indicate that the ΔICP is less than and greater than a certain threshold value). The dataset was then randomly divided into training (70%) and test (30%) datasets. The training set was utilized to train the random forest model. With the help of the test set, we comprehensively evaluated the detection efficiency of the model by calculating the AUC, sensitivity and specificity. The reference values chosen were normal ICP values and intracranial hypertension values (between 15 and 40 mmHg) because patients are often already in a state of intracranial hypertension at the time of initial monitoring, making it critical to explore the ability of the random forest algorithm to detect elevated ICP for different reference values. 

Variation of EIT signal with ICP In Fig. A, exemplary changes in EIT transmission impedance, ICP, and mean arterial pressure were presented throughout the course of the experiment, in which five injections of blood were performed, each of which resulted in a rapid rise in transmission impedance. At the same time, ICP decreased slowly after the rapid rise. The mean arterial pressure remained stable throughout the experiment without significant fluctuations. Figure B, on the other hand, shows the changes in ICP and ARV before and after a single blood injection. It can be observed that after the blood injection, ICP increased rapidly and then decreased slowly. At the same time, the amplitude of ARV decreased rapidly and then gradually increased. In order to investigate the effect of ICP changes on the perfusion EIT images, time points corresponding to three different ICP levels (i.e., t1, t2, and t3 in Fig. ) were selected for the EIT images. Each of these time points represented a cardiac cycle. Using the beginning of systole as the reference frame, differential imaging of the cardiac cycle at these three time points was performed. The results are shown in Fig. C. By observing the dynamic cerebral perfusion EIT images at the selected time points, we saw that the cerebral perfusion in the edge region of the images gradually decreased with the gradual increase in ICP. After removing the pulsation signal from the EIT using low-pass filtering, the base impedance was determined. Figure D shows the results of EIT-based impedance imaging for moments t1, t2, and t3, using t1 as the reference moment. The consecutive EIT images show that the blue region in the lower left corner represented the blood injection region. The impedance of this region gradually increased with the number of blood injections. Effect of ICP changes on EIT perfusion parameters Figure shows the mean perfusion parameters of all samples at different ICP levels. The Shapiro–Wilk test showed that the data obeyed a normal distribution ( P > 0.05). The application of Friedman test revealed significant differences ( P < 0.001) in perfusion parameters at different ICP levels. Subsequently, a Bonferroni post-hoc test was conducted to perform pairwise comparisons, and the results are presented in Fig. . The average statistical power was 0.94.The fEIT images in the lower part of the Fig. similarly reveal a trend of a gradual decrease in perfusion parameters in the peripheral regions of the image with increasing ICP. Table shows the mean Spearman correlation coefficients and standard deviations between ICP and each perfusion parameter. It is obvious from the table that the correlation was significantly higher in the group above 22 mmHg than in the group below 22 mmHg ( P < 0.01). This suggests that at higher ICP levels, there is a stronger correlation between ICP and cerebral perfusion, i.e., the effect of ICP on CBF is more significant: as ICP rises further, CBF decreases more significantly. Evaluation of the detection efficacy of EIT perfusion parameters in the detection of elevated ICP The ability of ΔMV, ΔH s , ΔIV, and ΔA db as single variables to detect ICP elevation greater than 5 and 10 mmHg was assessed by ROC curve analysis (Fig. A, ). The AUCs of these variables for detecting ICP elevation greater than 5 mmHg are 0.80, 0.74, 0.74, and 0.72, respectively, and the AUCs for detecting ICP elevation greater than 10 mmHg are 0.86, 0.76, 0.73, and 0.73, respectively. The specific values of the AUC, sensitivity, and specificity for each of the parameters are presented in Table . This suggests that independent EIT perfusion parameters have some accuracy in the diagnosis of elevated ICP. In this study, a random forest model was used to detect ICP elevation, which achieved an AUC of 0.95, 95% sensitivity with 96% specificity for an ICP elevation of 5 mmHg (Fig. C). In detecting ICP elevation at 10 mmHg (Fig. D), the AUC was 0.96, with sensitivity and specificity of 95 and 97%. Figure demonstrates the performance of the random forest model for detecting ICP elevations over 5 and 10 mmHg at different reference values. Specifically, the model performed well in terms of AUC, sensitivity and specificity, with values exceeding 0.92. Notably, these performance metrics show an increasing trend as the reference value increases. 

In Fig. A, exemplary changes in EIT transmission impedance, ICP, and mean arterial pressure were presented throughout the course of the experiment, in which five injections of blood were performed, each of which resulted in a rapid rise in transmission impedance. At the same time, ICP decreased slowly after the rapid rise. The mean arterial pressure remained stable throughout the experiment without significant fluctuations. Figure B, on the other hand, shows the changes in ICP and ARV before and after a single blood injection. It can be observed that after the blood injection, ICP increased rapidly and then decreased slowly. At the same time, the amplitude of ARV decreased rapidly and then gradually increased. In order to investigate the effect of ICP changes on the perfusion EIT images, time points corresponding to three different ICP levels (i.e., t1, t2, and t3 in Fig. ) were selected for the EIT images. Each of these time points represented a cardiac cycle. Using the beginning of systole as the reference frame, differential imaging of the cardiac cycle at these three time points was performed. The results are shown in Fig. C. By observing the dynamic cerebral perfusion EIT images at the selected time points, we saw that the cerebral perfusion in the edge region of the images gradually decreased with the gradual increase in ICP. After removing the pulsation signal from the EIT using low-pass filtering, the base impedance was determined. Figure D shows the results of EIT-based impedance imaging for moments t1, t2, and t3, using t1 as the reference moment. The consecutive EIT images show that the blue region in the lower left corner represented the blood injection region. The impedance of this region gradually increased with the number of blood injections. 

Figure shows the mean perfusion parameters of all samples at different ICP levels. The Shapiro–Wilk test showed that the data obeyed a normal distribution ( P > 0.05). The application of Friedman test revealed significant differences ( P < 0.001) in perfusion parameters at different ICP levels. Subsequently, a Bonferroni post-hoc test was conducted to perform pairwise comparisons, and the results are presented in Fig. . The average statistical power was 0.94.The fEIT images in the lower part of the Fig. similarly reveal a trend of a gradual decrease in perfusion parameters in the peripheral regions of the image with increasing ICP. Table shows the mean Spearman correlation coefficients and standard deviations between ICP and each perfusion parameter. It is obvious from the table that the correlation was significantly higher in the group above 22 mmHg than in the group below 22 mmHg ( P < 0.01). This suggests that at higher ICP levels, there is a stronger correlation between ICP and cerebral perfusion, i.e., the effect of ICP on CBF is more significant: as ICP rises further, CBF decreases more significantly. 

The ability of ΔMV, ΔH s , ΔIV, and ΔA db as single variables to detect ICP elevation greater than 5 and 10 mmHg was assessed by ROC curve analysis (Fig. A, ). The AUCs of these variables for detecting ICP elevation greater than 5 mmHg are 0.80, 0.74, 0.74, and 0.72, respectively, and the AUCs for detecting ICP elevation greater than 10 mmHg are 0.86, 0.76, 0.73, and 0.73, respectively. The specific values of the AUC, sensitivity, and specificity for each of the parameters are presented in Table . This suggests that independent EIT perfusion parameters have some accuracy in the diagnosis of elevated ICP. In this study, a random forest model was used to detect ICP elevation, which achieved an AUC of 0.95, 95% sensitivity with 96% specificity for an ICP elevation of 5 mmHg (Fig. C). In detecting ICP elevation at 10 mmHg (Fig. D), the AUC was 0.96, with sensitivity and specificity of 95 and 97%. Figure demonstrates the performance of the random forest model for detecting ICP elevations over 5 and 10 mmHg at different reference values. Specifically, the model performed well in terms of AUC, sensitivity and specificity, with values exceeding 0.92. Notably, these performance metrics show an increasing trend as the reference value increases. 

Persistently elevated ICP can result in cerebral blood supply deficiency, a significant contributor to secondary brain injury . ICP monitoring has become an important part of brain monitoring after brain injury . However, existing ICP monitoring techniques have various limitations, which make it difficult to effectively realize real-time noninvasive ICP monitoring . EIT has outstanding advantages in terms of high temporal resolution, noninvasiveness, and bedside monitoring, making it a promising approach to overcome the limitations of the existing techniques. Previous studies , only focused on the relationship between ICP and EIT basal impedance, even though basal impedance is affected by many factors and has poor specificity. Therefore, based on the effect of ICP on CBF , this study attempted to indirectly assess the changes in ICP by monitoring the cerebral perfusion status through EIT, opening new perspectives and methods for ICP monitoring. In this study, we explored the potential application of dynamic cerebral perfusion EIT in real-time monitoring of ICP changes by constructing an intracranial hypertension model in swine. Through EIT reconstructed images, we visualized the cerebral perfusion changes under different ICP levels. Subsequently, we extracted the parameters related to blood pulsatility from ARV and analyzed the patterns of these parameters with intracranial pressure levels. In addition, we explored the potential of EIT perfusion parameters in detecting elevated ICP. Changes in cerebral perfusion status with ICP can be observed by EIT In Fig. A, the changes of ICP and basal impedance with the progress of the experiment can be observed: the ICP increased rapidly during each blood injection, and decreased rapidly and then slowly after the end of the blood injection. This change in ICP became more and more obvious as the experiment progressed, and the baseline ICP gradually increased from 10 mmHg to about 20 mmHg. This indicates that the blood injection will lead to a significant increase in ICP, and after the blood injection is completed, due to the influence of the animals’ own ICP compensatory mechanism, their ICP will gradually fall back, but the overall trend is still rising. The basal impedance of the brain increased rapidly after each blood injection , but did not decrease significantly after the completion of blood injection, and its baseline gradually increased with the increase of blood injection. Meanwhile, the relationship between ICP and the dynamic perfusion signal is more direct: as can be seen from the dynamic perfusion signal (ARV) in Fig. B, the amplitude of the ARV decreased with the increase of ICP and gradually rebounded when ICP decreased. In a further study, we analyzed the potential application of utilizing dynamic CBF perfusion EIT image parameters in ICP monitoring. We observed in dynamic cerebral perfusion EIT images (Fig. C) that intracranial hypertension (25–40 mmHg) resulted in a significant decrease in peripheral cerebral perfusion relative to normal ICP levels (10 mmHg), and that the higher the ICP, the more pronounced the decrease. This result suggests that elevated ICP directly leads to a decrease in CBF in these regions and that this change can be detected by the EIT. In further analysis, we used a number of perfusion parameters aiming to quantitatively describe the perfusion status of CBF. By comparing the cerebral perfusion parameters at different ICP levels (Fig. ), it can be seen that all these parameters decreased significantly with increasing ICP, further confirming the sensitivity and effectiveness of EIT in monitoring ICP fluctuations. The fEIT images of each parameter shown at the bottom of Fig. present in detail the trends of each cerebral perfusion parameter at different ICP levels, while providing information about the perfusion parameters of the global intracranial region. Notably, the significant differences in the perfusion parameters in the comparison of ICP levels suggest that the correlation of these parameters with ICP changes may vary at different stages. In order to study the correlation between ICP and perfusion parameters in more depth, we divided the dataset into two intervals according to whether the ICP exceeded the therapeutic threshold of 22 mmHg  and analyzed the relationship between ICP and perfusion parameters within these two intervals separately. The results showed that in the interval where the ICP exceeded 22 mmHg, a significant negative correlation between ICP and perfusion parameters was demonstrated, with a correlation coefficient of approximately −0.8. In the interval where the ICP did not exceed this threshold, this negative correlation was weaker, with a correlation coefficient of approximately −0.2. In addition, the difference of the correlation between the two intervals was statistically significant ( P < 0.01), suggesting the stronger association between ICP and perfusion parameters in the state of intracranial hypertension. This trend might be due to the fact that the cerebral autoregulation mechanism was able to maintain the stability of CBF during the initial rise in ICP (<22 mmHg) . However, once ICP reached a certain threshold, this self-regulatory ability was limited and cerebral perfusion decreased significantly as ICP continued to rise . This suggest that the EIT is more appropriate for monitoring the changing status of ICP in patients with intracranial hypertension. EIT perfusion parameters promising for ICP change volume assessment By performing ROC curve analysis on a single parameter, we revealed its potential value in detecting ICP rise. Moreover, by evaluating the ability of EIT to detect ICP elevations greater than 10 mmHg, superior results were obtained compared to a previous study by Yang . In addition, we explored the performance of the EIT in detecting ICP rises greater than 5 mmHg, which likewise demonstrated its good detecting ability. In addition, a random forest model was developed to detect ICP elevation, and its AUC, sensitivity and specificity for detecting ICP elevation greater than 10 mmHg were 0.96, 95 and 97%. To further examine the ability of the random forest model to detect ICP at different intracranial pressure levels, 15–40 mmHg were selected as baseline reference values in this study. The efficiency of the model in detecting ICP changes with increases of 5 and 10 mmHg relative to these baseline values was evaluated. The model’s AUC, sensitivity, and specificity exceeded 0.92 at all selected reference values. The results show that the EIT parameters are capable of detecting ICP elevation, which lays a solid foundation for the development and optimization of related diagnostic techniques in the future. Limitations of ICP monitoring based on EIT basal impedance It is noteworthy that in our study, the plateau period of both cerebral basal impedance and ICP increased with increasing blood injection, showing a positive correlation, which is different from the negative correlation between basal impedance and ICP obtained by Yang et al. . This discrepancy may be due to the fact that in the study of Yang et al., the dehydration process utilized the high osmotic pressure properties of the dehydrating agent to allow water molecules in the extracellular fluid in the vicinity of the microvessels to enter the vasculature , which led to a reduction in the cellular interstitial component and decreased its electrical conductivity, causing the brain’s basal impedance to rise with decreasing ICP. In contrast, after injection of un-anticoagulated blood into the brain parenchyma, the increase in the contents of the cranial cavity caused an increase in ICP, which was compensated for by the absorption of cerebrospinal fluid (CSF) . Since the resistivity of blood is higher than that of CSF, this led to the phenomenon of cerebral basal impedance rising with ICP in this study . This phenomenon suggests that the relationship between the basal impedance of brain tissue and ICP may vary in different clinical applications. Therefore, the method of ICP monitoring based on dynamic perfusion impedance parameters has significant potential because the relationship between dynamic perfusion impedance parameters and ICP is more direct in this method, which is important for the application of EIT in monitoring ICP. Advantages and limitations of perfusion EIT for monitoring ICP In view of the inherent risks associated with invasive ICP measurement, a wide range of non-invasive methodologies have been investigated . These methods typically assess physiological alterations that are indirectly associated with ICP, and can be classified into four principal categories: fluid dynamics, ophthalmology, ear, and electrophysiology . In this study, we employed EIT to assess cerebral perfusion, thereby enabling us to estimate changes in ICP. Consequently, EIT can be classified as one of the fluid dynamics methods. This method is similar to TCD and Near-Infrared Spectroscopy (NIRS) , both of which estimate ICP levels by measuring changes in parameters related to CBF. However, the application of NIRS for ICP measurement is constrained by the availability of NIRS equipment, with approximately 50% of records failing to meet the requisite reliability standards . In contrast to TCD, which can only detect specific large blood vessels , EIT can provide comprehensive information on the perfusion status of the entire brain. Furthermore, EIT is able to monitor continuously for extended periods of time, which is a capability that TCD and the majority of other non-invasive monitoring techniques lack . It can be reasonably concluded that EIT represents an efficacious bedside monitoring instrument, capable of furnishing timely alerts regarding elevated ICP. This functionality enables prompt intervention and treatment by medical personnel, thereby reducing the necessity for invasive intracranial pressure monitoring techniques and minimizing patient risk and discomfort. There are several limitations to this study due to the conditions. (1) During prolonged monitoring, although the observed cerebral perfusion parameters were directly affected by changes in ICP, they were theoretically also affected by blood pressure fluctuations. Given the stability of blood pressure could not be fully controlled during the experiment, this somewhat limited the feasibility and reliability of the EIT-ICP correlation analysis. Future studies should consider the implementation of simultaneous blood pressure continuous monitoring, which will allow for more precise adjustment and interpretation of effects caused by blood pressure fluctuations during data analysis. (2) In the results of this study, we focused on evaluating the ability of the EIT to detect elevated ICP without delving into the absolute value of ICP. This decision was made primarily to account for the effects of interindividual variability, which might potentially blur the accurate assessment of the absolute value of ICP. Therefore, future studies should focus on developing and optimizing EIT parameters that reflect individual ICP characteristics to reduce the impact of individual variability. (3) Although this study demonstrated that EIT perfusion parameters can be used for the monitoring of ICP changes, the small sample size might limit the statistical power of the study and the broad applicability of the results. Therefore, it is still necessary to promote the clinical application of related methods by larger sample sizes in future studies. (4) Although this study has demonstrated the potential of EIT to monitor ICP through animal experiments, several practical issues remain to be resolved before it can be applied in clinical practice. For instance, in animal experiments, electrodes are penetrated through the skull to enhance signal stability; however, in clinical practice, non-invasive scalp electrodes are typically preferred. Furthermore, other medical devices in the ICU environment may generate electromagnetic interference, which could impair the quality of the EIT signal. Although some shielding methods are currently available to reduce the impact of external interference, these factors still need to be further studied and optimized in order to better promote the clinical application of EIT. An intracranial hypertension animal model was constructed by injecting blood into a specific area, which may not fully reflect the specific conditions of various complex areas in a clinical environment. It is therefore evident that, in order to facilitate a more accurate comprehension and implementation of these findings, the collection of additional clinical data is imperative for subsequent in-depth analysis. 

In Fig. A, the changes of ICP and basal impedance with the progress of the experiment can be observed: the ICP increased rapidly during each blood injection, and decreased rapidly and then slowly after the end of the blood injection. This change in ICP became more and more obvious as the experiment progressed, and the baseline ICP gradually increased from 10 mmHg to about 20 mmHg. This indicates that the blood injection will lead to a significant increase in ICP, and after the blood injection is completed, due to the influence of the animals’ own ICP compensatory mechanism, their ICP will gradually fall back, but the overall trend is still rising. The basal impedance of the brain increased rapidly after each blood injection , but did not decrease significantly after the completion of blood injection, and its baseline gradually increased with the increase of blood injection. Meanwhile, the relationship between ICP and the dynamic perfusion signal is more direct: as can be seen from the dynamic perfusion signal (ARV) in Fig. B, the amplitude of the ARV decreased with the increase of ICP and gradually rebounded when ICP decreased. In a further study, we analyzed the potential application of utilizing dynamic CBF perfusion EIT image parameters in ICP monitoring. We observed in dynamic cerebral perfusion EIT images (Fig. C) that intracranial hypertension (25–40 mmHg) resulted in a significant decrease in peripheral cerebral perfusion relative to normal ICP levels (10 mmHg), and that the higher the ICP, the more pronounced the decrease. This result suggests that elevated ICP directly leads to a decrease in CBF in these regions and that this change can be detected by the EIT. In further analysis, we used a number of perfusion parameters aiming to quantitatively describe the perfusion status of CBF. By comparing the cerebral perfusion parameters at different ICP levels (Fig. ), it can be seen that all these parameters decreased significantly with increasing ICP, further confirming the sensitivity and effectiveness of EIT in monitoring ICP fluctuations. The fEIT images of each parameter shown at the bottom of Fig. present in detail the trends of each cerebral perfusion parameter at different ICP levels, while providing information about the perfusion parameters of the global intracranial region. Notably, the significant differences in the perfusion parameters in the comparison of ICP levels suggest that the correlation of these parameters with ICP changes may vary at different stages. In order to study the correlation between ICP and perfusion parameters in more depth, we divided the dataset into two intervals according to whether the ICP exceeded the therapeutic threshold of 22 mmHg  and analyzed the relationship between ICP and perfusion parameters within these two intervals separately. The results showed that in the interval where the ICP exceeded 22 mmHg, a significant negative correlation between ICP and perfusion parameters was demonstrated, with a correlation coefficient of approximately −0.8. In the interval where the ICP did not exceed this threshold, this negative correlation was weaker, with a correlation coefficient of approximately −0.2. In addition, the difference of the correlation between the two intervals was statistically significant ( P < 0.01), suggesting the stronger association between ICP and perfusion parameters in the state of intracranial hypertension. This trend might be due to the fact that the cerebral autoregulation mechanism was able to maintain the stability of CBF during the initial rise in ICP (<22 mmHg) . However, once ICP reached a certain threshold, this self-regulatory ability was limited and cerebral perfusion decreased significantly as ICP continued to rise . This suggest that the EIT is more appropriate for monitoring the changing status of ICP in patients with intracranial hypertension. 

By performing ROC curve analysis on a single parameter, we revealed its potential value in detecting ICP rise. Moreover, by evaluating the ability of EIT to detect ICP elevations greater than 10 mmHg, superior results were obtained compared to a previous study by Yang . In addition, we explored the performance of the EIT in detecting ICP rises greater than 5 mmHg, which likewise demonstrated its good detecting ability. In addition, a random forest model was developed to detect ICP elevation, and its AUC, sensitivity and specificity for detecting ICP elevation greater than 10 mmHg were 0.96, 95 and 97%. To further examine the ability of the random forest model to detect ICP at different intracranial pressure levels, 15–40 mmHg were selected as baseline reference values in this study. The efficiency of the model in detecting ICP changes with increases of 5 and 10 mmHg relative to these baseline values was evaluated. The model’s AUC, sensitivity, and specificity exceeded 0.92 at all selected reference values. The results show that the EIT parameters are capable of detecting ICP elevation, which lays a solid foundation for the development and optimization of related diagnostic techniques in the future. 

It is noteworthy that in our study, the plateau period of both cerebral basal impedance and ICP increased with increasing blood injection, showing a positive correlation, which is different from the negative correlation between basal impedance and ICP obtained by Yang et al. . This discrepancy may be due to the fact that in the study of Yang et al., the dehydration process utilized the high osmotic pressure properties of the dehydrating agent to allow water molecules in the extracellular fluid in the vicinity of the microvessels to enter the vasculature , which led to a reduction in the cellular interstitial component and decreased its electrical conductivity, causing the brain’s basal impedance to rise with decreasing ICP. In contrast, after injection of un-anticoagulated blood into the brain parenchyma, the increase in the contents of the cranial cavity caused an increase in ICP, which was compensated for by the absorption of cerebrospinal fluid (CSF) . Since the resistivity of blood is higher than that of CSF, this led to the phenomenon of cerebral basal impedance rising with ICP in this study . This phenomenon suggests that the relationship between the basal impedance of brain tissue and ICP may vary in different clinical applications. Therefore, the method of ICP monitoring based on dynamic perfusion impedance parameters has significant potential because the relationship between dynamic perfusion impedance parameters and ICP is more direct in this method, which is important for the application of EIT in monitoring ICP. 

In view of the inherent risks associated with invasive ICP measurement, a wide range of non-invasive methodologies have been investigated . These methods typically assess physiological alterations that are indirectly associated with ICP, and can be classified into four principal categories: fluid dynamics, ophthalmology, ear, and electrophysiology . In this study, we employed EIT to assess cerebral perfusion, thereby enabling us to estimate changes in ICP. Consequently, EIT can be classified as one of the fluid dynamics methods. This method is similar to TCD and Near-Infrared Spectroscopy (NIRS) , both of which estimate ICP levels by measuring changes in parameters related to CBF. However, the application of NIRS for ICP measurement is constrained by the availability of NIRS equipment, with approximately 50% of records failing to meet the requisite reliability standards . In contrast to TCD, which can only detect specific large blood vessels , EIT can provide comprehensive information on the perfusion status of the entire brain. Furthermore, EIT is able to monitor continuously for extended periods of time, which is a capability that TCD and the majority of other non-invasive monitoring techniques lack . It can be reasonably concluded that EIT represents an efficacious bedside monitoring instrument, capable of furnishing timely alerts regarding elevated ICP. This functionality enables prompt intervention and treatment by medical personnel, thereby reducing the necessity for invasive intracranial pressure monitoring techniques and minimizing patient risk and discomfort. There are several limitations to this study due to the conditions. (1) During prolonged monitoring, although the observed cerebral perfusion parameters were directly affected by changes in ICP, they were theoretically also affected by blood pressure fluctuations. Given the stability of blood pressure could not be fully controlled during the experiment, this somewhat limited the feasibility and reliability of the EIT-ICP correlation analysis. Future studies should consider the implementation of simultaneous blood pressure continuous monitoring, which will allow for more precise adjustment and interpretation of effects caused by blood pressure fluctuations during data analysis. (2) In the results of this study, we focused on evaluating the ability of the EIT to detect elevated ICP without delving into the absolute value of ICP. This decision was made primarily to account for the effects of interindividual variability, which might potentially blur the accurate assessment of the absolute value of ICP. Therefore, future studies should focus on developing and optimizing EIT parameters that reflect individual ICP characteristics to reduce the impact of individual variability. (3) Although this study demonstrated that EIT perfusion parameters can be used for the monitoring of ICP changes, the small sample size might limit the statistical power of the study and the broad applicability of the results. Therefore, it is still necessary to promote the clinical application of related methods by larger sample sizes in future studies. (4) Although this study has demonstrated the potential of EIT to monitor ICP through animal experiments, several practical issues remain to be resolved before it can be applied in clinical practice. For instance, in animal experiments, electrodes are penetrated through the skull to enhance signal stability; however, in clinical practice, non-invasive scalp electrodes are typically preferred. Furthermore, other medical devices in the ICU environment may generate electromagnetic interference, which could impair the quality of the EIT signal. Although some shielding methods are currently available to reduce the impact of external interference, these factors still need to be further studied and optimized in order to better promote the clinical application of EIT. An intracranial hypertension animal model was constructed by injecting blood into a specific area, which may not fully reflect the specific conditions of various complex areas in a clinical environment. It is therefore evident that, in order to facilitate a more accurate comprehension and implementation of these findings, the collection of additional clinical data is imperative for subsequent in-depth analysis. 

In this study, we constructed an intracranial hypertension swine model. A high-performance EIT system recently developed by our team was utilized to collect information about weak impedance caused by the CBF. The EIT parameters reflecting the changes of the cerebral perfusion status were extracted, and possible effects of ICP elevations on cerebral perfusion impedance were analyzed. This is the first time to confirm the feasibility of EIT to achieve ICP elevation early warnings by monitoring the changes of the CBF status. The results demonstrate that EIT could be a promising bedside monitoring tool for real-time non-invasive monitoring and early warning of ICP changes, which may have great potential for clinical application. 

