Application of Cerebral Electrical Impedance Tomography in Monitoring a Child with Severe Encephalitis: A Case Report and Literature Review

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Methods : We present a case of a child with severe encephalitis and status epilepticus. Bedside EIT (EH-300 system) monitoring was conducted continuously for four days. Quantitative EIT parameters including whole-brain impedance, regional impedance, and balance degree index were analyzed. Temporal and spatial variations in these parameters were compared with findings from cranial CT and MRI. Results : Initial cranial CT revealed no definite structural abnormalities. In contrast, EIT detected early pathophysiological alterations, demonstrating significant prefrontal impedance asymmetry (anteroposterior balance degree BD1: 15.30%) and a progressive decrease in right prefrontal impedance (total R1IMP change rate: -19.81%). Subsequent MRI confirmed multiple abnormal signals in both cerebral hemispheres; the lesion distribution showed high spatial consistency with the regional abnormalities earlier identified by EIT. Furthermore, EIT captured dynamic fluctuations that were not assessable through real-time conventional imaging. Conclusion : Employing well-defined quantitative algorithms, cerebral EIT can identify early functional abnormalities prior to the detection of structural changes on conventional imaging. Its dynamic monitoring capability provides a valuable bedside tool for early warning, lesion localization, and therapeutic response assessment in severe encephalitis. This study was approved by the Hospital Ethics Management Committee (Approval No: 2025-TEC-0014). Written informed consent was obtained from the patient’s legal guardian(s) for publication of this case report and any accompanying images. Cerebral electrical impedance tomography Severe encephalitis Calculation rules Magnetic resonance imaging Bedside monitoring Pediatric Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Severe encephalitis is a critical pediatric condition often complicated by status epilepticus, leading to serious pathophysiological changes such as cerebral edema and intracranial hypertension. Prognosis depends on early recognition and dynamic management of brain injury. Although computed tomography (CT) and magnetic resonance imaging (MRI) remian the gold standards for assessing structural brain abnormalities, CT exposes children to ionizing radiation, and MRI requires transporting critically ill children and cannot provide real-time bedside assessment. This limitations hinder continuous monitoring with both modalities. Cerebral electrical impedance tomography (EIT) is an emerging non-invasive functional imaging technology that measures changes in the electrical conductivity of brain tissue, thereby capturing real-time alterations in intracranial fluid content and distribution associated with cerebral edema, ischemia, or hyperemia [1]. The core advantage of EIT lies in its ability to translate physiological signals into a series of quantitative parameters with clear physical and clinical significance, all derived through strict algorithms. Theoretically, EIT may detect changes in cerebral fluid dynamics earlier than the morphological changes visible on conventional imaging. This study, based on continuous EIT monitoring data from a pediatric case of severe encephalitis and with clear elaboration of parameter algorithms, systematically compares EIT findings with CT/MRI results. It aims to validate the clinical value of EIT in pediatric neurocritical care, particularly its capabilities for early warning and dynamic monitoring. 1. Clinical Data Patient information: A 5-year-and-10-month-old male was admitted to our Pediatric Intensive Care Unit (PICU) on August 12, 2025, presenting with "fever and cough for 4 days, and convulsions for 3 days." The patient developed fever and cough 4 days prior to admission, followed by recurrent convulsions 1 day after the fever onset. The seizures manifested as loss of consciousness, strabismus, incontinence, and limb weakness, each episode lasting approximately 3-5 minutes and resolving spontaneously. He was initially admitted to an external hospital and received symptomatic treatments including antibiotics, antivirals, intracranial pressure reduction, and antiepileptics (midazolam, valproate, levetiracetam). However, recurrent seizures persisted, prompting transfer to our PICU. Physical examination upon admission revealed a Glasgow Coma Scale (GCS) score of 7 (E1V2M4), supple neck, positive bilateral Babinski signs, present tendon reflexes, and unremarkable cardiopulmonary and abdominal findings. Relevant laboratory tests were performed. Cerebrospinal fluid (CSF) routine, biochemistry, culture, and metagenomic next-generation sequencing (mNGS) showed no significant abnormalities. Bedside electroencephalography (EEG) indicated an interictal background with diffuse delta and theta activity. A 14-hour recording captured three seizure-onset electrographic changes originating from the left posterior head region, each lasting 4 to 6 minutes, suggesting electrographic or electroclinical seizures (ESz/ECSZ). Imaging findings: Head CT (August 12, 2025): No significant abnormal density foci were observed on plain scan. Midline structures were preserved. Head MRI (August 14, 2025, see Figure 3): Multiple patchy, slightly long T1 and T2 signal foci were observed in both cerebral hemispheres, appearing slightly hyperintense on Fluid Attenuated Inversion Recovery (FLAIR), predominantly in the cortex. Some lesions showed restricted diffusion on DWI. These findings indicated multiple abnormal signals consistent with encephalitis. Continuous cerebral electrical impedance tomography (EIT) monitoring was initiated upon admission. Clinical diagnoses: (1) Severe encephalitis; (2) Status epilepticus; (3) Acute bronchopneumonia. The patient received supportive treatment for 17 days, including oxygen therapy, aggressive intracranial pressure management, intravenous immunoglobulin, high-dose steroids (methylprednisolone) for immunomodulation, and multiple antiepileptic drugs. His condition improved, with a GCS score of 13 (E4V4M5) and cessation of seizures. He was subsequently transferred to the rehabilitation ward for continued care for 24 days before discharge. 2. Monitoring methodology, data acquisition, and analysis 1) Device and d ata a cquisition: A cerebral EIT system (model EH-300) was employed. The measurement obtained at 19:33 on August 12, 2025, was designated as the baseline. Continuous monitoring was conducted until August 15, with complete data acquired from four distinct time points. Parameters Definition and Calculation Rules: All EIT parameters were calculated according to the following formulas.[Insert Table 1 here] Table 1. Summary of cerebral impedance parameters Parameter Category Parameter Name (Abbreviation) Definition and Calculation Formula Clinical Significance Baseline Impedance Global Impedance (GIMP) GIMP = (LIMP + RIMP) / 2 Global Baseline Electrical Conductivity Baseline Impedance Left/Right Hemisphere Impedance (LIMP/RIMP) Device-Measured Raw Impedance Values of Left/Right Hemisphere Left/Right Hemisphere Global Fluid Status Regional Impedance Left/Right Prefrontal (L1IMP/R1IMP) Device-Measured Impedance Values in Corresponding Regions of Interest Frontal Cortex Fluid Status Regional Impedance Left/Right Parietal-Basal Ganglia (L2IMP/R2IMP) Device-Measured Impedance Values in Corresponding Regions of Interest Parietal-Basal Fluid Status Regional Impedance Left/Right Occipito-Cerebellar (L3IMP/R3IMP) Device-Measured Impedance Values in Corresponding Regions of Interest Occipito-Cerebellar Fluid Status Rate of Change Daily Rate of Change/Cumulative Rate of Change Rate of Change = [Current Value - Previous Value (or Baseline Value)] / Previous Value (or Baseline Value) × 100% Quantify the temporal variation in impedance Balance Degree Global Hemispheric Balance Degree (BD) BD = |LIMP - RIMP| / GIMP × 100% Assess Asymmetry Between Left and Right Cerebral Hemispheres Balance Degree Anterior Balance Degree (BD1) BD1 = |L1IMP - R1IMP| / GIMP × 100% Assess Frontal Lobe Symmetry Balance Degree Central Balance Degree (BD2) BD2 = |L2IMP - R2IMP| / GIMP × 100% Assess Parietal-Basal Ganglia Symmetry Balance Degree Posterior Balance Degree (BD3) BD3 = |L3IMP - R3IMP| / GIMP × 100% Assess Occipito-Cerebellar Symmetry 2) Imaging and a lert r ules Color c oding for i maging: Using the patient's initial impedance as the baseline, dynamic relative changes (ΔZ) were displayed. Red indicates decreased impedance (increased conductivity, suggestive of elevated fluid content, such as edema), while b lue indicates an increase in impedance (decreased conductivity, suggestive of reduced fluid content, such as ischemia). Alert t hresholds ( p ediatric): Based on pediatric reference standards, a change rate ≥ |±15%| triggers a yellow alert (Level Ⅰ, requiring attention), and a change rate ≥ |±20%| triggers a red alert (Level Ⅱ, indicating high risk and prompting immediate imaging reassessment). 3) Imaging c orrelation a nalysis Temporal correlation: The EIT monitoring timeline was aligned with the timestamps of CT and MRI examinations to analyze EIT trends before and after each imaging study. Spatial localization correlation: Anatomical correlation was performed by comparing regions showing abnormal impedance changes on EIT with areas of abnormal signals on MRI. 4) Results: Presentation of EIT dynamic monitoring data and calculated parameters.[Insert Table 2 here] Table 2. Cerebral EIT Continuous Monitoring Data (Derived from Calculation Formulas): Longitudinal Record with Optimal Signal Points No. Time Point GIMP LIMP RIMP L1IMP L2IMP L3IMP R1IMP R2IMP R3IMP BD BD1 BD2 BD3 1 08/12/2025 19:33:04 11.25 11.36 11.14 13.13 10.19 11.04 11.41 11.98 10.30 1.96% 15.30% -15.90% 6.58% 2 08/13/2025 12:54:24 11.59 11.24 11.94 11.86 10.10 12.24 10.24 12.76 12.75 -6.04% 13.96% 22.94% 4.61% 3 08/14/2025 17:44:13 11.13 11.13 11.92 11.47 9.41 12.38 9.98 15.33 12.76 -7.10% 13.38% -53.22% -3.04% 4 08/15/2025 16:40:59 11.65 11.76 11.55 11.18 10.32 13.93 9.15 11.27 13.93 1.80% 17.42% -8.15% 0.00% Note: Values for BD, BD1, BD2, and BD3 were recalculated according to the aforementioned formulas. Minor discrepancies from the raw data exist due to rounding of the original measurements. This table presents the recalculated values to demonstrate the accuracy of the calculation rules. For instance:BD1(8/12) = ⎜13.13-11.41⎟ / 11.25 * 100% = 1.72 / 11.25 * 100% ≈ 15.29%. Table 3. Longitudinal Impedance Rate of Change Calculations (Derived from the Rate of Change Formula): Baseline Impedance and Rate of Change Data Impedance Type 08/12 Baseline Value 08/13 Baseline Value 08/13 Daily Rate of Change 08/14 Baseline Value 08/14 Daily Rate of Change 08/15 Baseline Value 08/15 Daily Rate of Change Cumulative Rate of Change (08/12→08/15) GIMP 11.25 11.59 +3.02% 11.13 -3.97% 11.65 +4.67% +3.56% LIMP 11.36 11.24 -1.06% 11.13 -0.98% 11.76 +5.66% +3.52% RIMP 11.14 11.94 +7.18% 11.92 -0.17% 11.55 -3.10% +3.68% L1IMP 13.13 11.86 -9.67% 11.47 -3.29% 11.18 -2.53% -14.85%▼ L2IMP 10.19 10.10 -0.88% 9.41 -6.83% 10.32 +9.67% +1.28% L3IMP 11.04 12.24 +10.87% 12.38 +1.14% 13.93 +12.52% +26.18%▲ R1IMP 11.41 10.24 -10.25%▼ 9.98 -2.54% 9.15 -8.32%▼ -19.81%▼ R2IMP 11.98 12.76 +6.51% 15.33 +20.14%▲ 11.27 -26.48%▼ -5.93% R3IMP 10.30 12.75 +23.79%▲ 12.76 +0.08% 13.93 +9.17% +35.24%▲ Note: ▼ indicates a significant decrease (rate of change ≤ -5%); ▲ indicates a significant increase (rate of change ≥ +5%). Values in red font exceed the pediatric red alert threshold (≥ |±20%| ). All rates of change were calculated using the formula: (Current Value - Previous Value) / Previous Value × 100%. Table 4. Temporal Correlation Between EIT Monitoring and Imaging Findings Date Imaging Findings Core EIT Abnormalities and Alert Levels Comparative Analysis and Clinical Significance 08/12 CT: "No significant intracranial abnormalities." Prefrontal Asymmetry: BD1 (15.30%) was significantly elevated. Calculation Basis: BD1 = ⎜L1IMP - R1IMP⎟ / GIMP = ⎜13.13-11.41⎟ / 11.25 ≈ 15.30% Early Warning: EIT detected abnormalities in prefrontal fluid distribution through functional parameters (Balance Degree) when CT showed no structural abnormalities, demonstrating its capability for pre-symptomatic alert. 08/13 No Imaging Prefrontal Lobe: R1IMP Daily Rate of Change: -10.25% (yellow alert) Occipito-Cerebellar: R3IMP Daily Rate of Change: +23.79% (red alert) Calculation Basis: /11.41×100%=-10.25% /10.30×100%=+23.79% Dynamic Deterioration: EIT monitoring captured significant functional deterioration across multiple brain regions, providing a strong indication for subsequent MRI examination the following day. 08/14 MRI： "Multiple abnormal signals in both cerebral hemispheres." Central Region: R2IMP Daily Rate of Change: +20.14% (red alert) BD2 Extreme Value: -53.22% Calculation Basis: BD2=⎜L2IMP-R2IMP⎟/GIMP=⎜9.41-15.33⎟/11.13≈-53.22% Spatial Validation: The abnormal signals identified on MRI showed a high degree of spatial concordance with the regions of significant impedance change and balance disturbance localized by EIT. 08/15 No Imaging Central Region: R2IMP Daily Rate of Change: -26.48% (red alert) Calculation Basis: /15.33×100%=-26.48% Pathological Evolution: EIT captured a reversal in R2IMP from a sharp increase to a decrease, revealing a dynamic pathological process undetectable by conventional imaging. Note: The temporal correlation analysis between EIT and the imaging gold standard demonstrates the clinical value of EIT, as the timing of EIT parameter changes shows a clear relationship with the emergence of imaging findings. Key parameter trends and functional imaging visualization Figure 1 shows the key regional impedance trends. [Insert Figure 1 here] Figure 2 provides a comparative schematic of EIT functional imaging and MRI structural imaging. [Insert Figure 2 here] The regional impedance curves are displayed in Figure 4. [Insert Figure 4 here] 5). Signal quality control and baseline strategy analysis: We fully recognize the challenges associated with long-term EIT monitoring in the PICU environment, including artifacts introduced by patient position changes, clinical procedures (such as suctioning), and electrode reapplication. To maximize data reliability, the following measures were implemented: a.Stabilization procedures: All electrodes were applied by a trained specialist using uniformly standardized electrodes and conductive gel, and were secured with an elastic headband to minimize displacement. Data acquisition was performed during periods when the patient was relatively stable and no intensive clinical procedures were underway. b. Signal quality assessment: The device incorporates a built-in Signal Quality Index (SQI). We exclusively acquired and analyzed Only data points with SQI values above a preset threshold (80%) were included in the analysis to exclude low-quality signals caused by poor contact or significant body movement. c. Strategy for managing electrode reapplication: Electrodes reapplication after the patient returned from CT/MRI examinations introduced baseline drift. To address this, we adopted a dual-baseline analysis strategy: Absolute baseline (Initial value from August 12): Used to assess the overall trend from admission (Cumulative rate of change). Relative/segmented baseline: For data following electrode reapplication (e.g., measurements on August 13, 14), the daily rate of change was calculated using the preceding measurement as the reference (e.g., the August 13 value as the baseline for August 14). This differential approach, based on adjacent time points, effectively mitigates systematic drift caused by absolute changes in electrode position and provided a more sensitive reflection of the true pathophysiological dynamics occurring between the two measurement periods. 3. Discussion Electrical impedance tomography (EIT) is a non-invasive, radiation-free functional imaging technique that estimates the spatial distribution of tissue resistivity within the body by measuring transfer impedance between surface electrodes [ 2 , 3 ]. Different tissues exhibit distinct impedance levels, which vary under physiological and pathological conditions. Consequently, EIT can detect impedance changes resulting from such physiological and pathological shifts [ 4 ]. EIT image reconstruction methods primarily include static and dynamic modes. Static reconstruction aims to recover the absolute distribution of electrical conductivity within tissues, whereas dynamic reconstruction utilizes measurement data from different time points and employs difference imaging algorithms to reconstruct relative images of conductivity change. In dynamic reconstruction, differential processing effectively mitigates model errors, thereby significantly reducing their impact on the system's imaging accuracy [ 2 ]. This advantage makes dynamic reconstruction particularly valuable for bedside monitoring. EIT technology has been investigated in various clinical contexts, including pulmonary ventilation and perfusion imaging [ 5 ], brain functional imaging [ 2 ], and abdominal organ functional imaging [ 6 ]. Its applications extend to conditions such as breast cancer, acute respiratory distress syndrome, and stroke [ 7 – 9 ]. Furthermore, EIT utilizes low-frequency alternating currents (typically 1 kHz – 1 MHz) that safely penetrate human tissues without ionizing effects, ensuring good biosafety. The significance of applying EIT to cerebral studies stems from the high mortality rate associated with brain injuries and their severe impact on quality of life. Its imaging mechanism relies on the principle that neuronal depolarization alters the electrical conductivity of the extracellular environment through the opening and closing of ion channels. EIT captures these bioelectrical impedance signals to generate functional imaging of cerebral activity [ 10 ]. An inherent electrical property difference exists between brain tissue and blood, primarily manifested as higher electrical conductivity (i.e., lower electrical impedance) in blood. Under pathological conditions such as epileptic seizures, accompanied by neuronal hyperactivity and potential cellular edema, local cerebral blood volume undergoes significant changes. This, in turn, alters the overall electrical impedance characteristics of the cerebral cortex [ 1 ]. A key advantage of EIT lies in its ability to provide a means for long-term, repeatable monitoring of these neuronal activity-related impedance fluctuations, effectively avoiding interference from artifacts such as motion [ 11 ]. However, significant electrical conductivity heterogeneity exists among intracranial tissues. Cerebrospinal fluid exhibits high conductivity (low resistance), whereas the skull bone shows extremely low conductivity (high resistance). This complex electrical environment poses considerably greater technical challenges for cerebral EIT imaging compared with its application in other organ systems [ 12 – 13 ]. Currently, cerebral EIT monitoring is primarily applied in conditions such as stroke, epilepsy, and cerebral edema. Normal, ischemic, and hemorrhagic brain tissues exhibit distinguishable electrical property differences on electrical impedance spectra [ 14 ]. Based on this principle, Holder et al. investigated the potential of multi-frequency EIT for the diagnosis and differential diagnosis of stroke. Their studies not only identified optimized modes suitable for stroke imaging but also confirmed the feasibility of using this technology for early stroke diagnosis, imaging assessment, and even guiding thrombolytic therapy [ 15 – 17 ]. In epilepsy applications, Holder et al. reported pioneering work using EIT for functional neural activity imaging. Through subdural electrode recordings, they successfully localized epileptogenic foci, demonstrating the potential of EIT in this field [ 18 ]. The present case primarily focused on assessing cerebral edema associated with encephalitis. Fu et al. were the first to report that EIT could be used for real-time, non-invasive, dynamic monitoring of focal cerebral edema during clinical dehydration therapy [ 19 ]. Regarding fundamental research, the Song team systematically monitored cerebral electrical impedance and morphological changes across different phases following ischemic brain injury in a rat model of cerebral edema. Their study found a significant increase in brain tissue resistivity at 6 hours post-injury, which subsequently decreased between 6 and 24 hours. This temporal sequence of impedance changes confirms the potential of EIT for continuously tracking the evolution of cerebral edema [ 20 ]. Conventionally, clinical assessment of brain injury primarily relies on techniques such as CT, MRI, and PET. However, both CT and PET involve ionizing radiation during imaging, while MRI has limitations including longer scanning times and higher demands on patient cooperation. Furthermore, such equipment is typically expensive, has poor hardware mobility, and offers limited flexibility for repeated examinations. In contrast, EIT offers advantages such as lower cost, high reproducibility, ease of operation, and minimal adverse effects. In recent years, with continuous technological refinement and device updates, EIT has gradually been applied to the early diagnosis and personalized treatment of pediatric-related diseases. The parameter system of EIT, grounded in well-defined calculation rules, confers significant early warning value. First, in this case, EIT triggered an alert based on an elevated prefrontal balance degree (BD1) despite a negative CT scan on August 12. Balance degree indices like BD1, computed using simple relative difference formulas, effectively eliminate interference from individual baseline impedance variations and amplify localized asymmetric signals. This demonstrates EIT's sensitivity to subtle changes in the cerebral microenvironment, allowing functional monitoring to bridge the "temporal blind zone" between physiological abnormality and the manifestation of morphological changes. Second, the regional impedance rates of change and balance degree indices derived from calculations showed high spatial concordance with MRI findings. The "multiple abnormal signals in both cerebral hemispheres" confirmed by MRI corresponded well with the abnormal regions identified by EIT calculations, including decreased prefrontal impedance, sharply fluctuating parietal-basal ganglia impedance, and elevated occipito-cerebellar impedance. This indicates that EIT parameters, based on explicit ROIs and calculation formulas, can not only provide early warning of global abnormalities but also accurately localize them to specific functional areas within the cerebral hemispheres. Furthermore, EIT provides an irreplaceable dynamic monitoring perspective through continuous calculation of rates of change. In this case, the substantial bidirectional fluctuations of the right parietal-basal ganglia impedance (R2IMP), calculated according to the rate of change formula, vividly illustrated the complexity of local pathological processes in encephalitis. This real-time, continuously computed "pathophysiological movie," based on strict mathematical calculations, cannot be provided by other intermittent imaging modalities. 4. Conclusion In this case, continuous bedside cerebral EIT monitoring, leveraging its parameter system with well-defined calculation rules (including baseline impedance, rate of change, and balance degree indices), proved to be an effective and reliable bedside functional monitoring tool. By quantifying these parameters, EIT demonstrated the ability to detect early abnormalities in cerebral functional status prior to their manifestation on CT, while also showing good concordance with structural lesions identified by MRI. Its core value lies in translating complex cerebral fluid dynamics into quantifiable, dynamic trend data, thereby enabling more timely and precise personalized treatment. As a novel functional imaging modality, cerebral EIT shows considerable potential for real-time, non-invasive monitoring of children with severe intracranial infections or brain injury in the PICU. It can assist clinicians in promptly adjusting treatment strategies and potentially improving patient outcomes. This technique is non-invasive and free from ionizing radiation. Although its application has long been primarily confined to research settings, recent clinical practice increasingly supports its practical value. The potential of EIT in broader clinical scenarios is now being actively explored. With continued validation and integration into clinical workflows, cerebral EIT is expected to become an important adjunctive tool in critical care medicine [ 21 ]. Declarations Ethics approval and consent to participate This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Hospital Ethics Management Committee (Approval No: 2025-TEC-0014). Written informed consent was obtained from the patient's legal guardian(s) for the patient's participation in the monitoring and for the analysis/publication of the anonymized data. Consent for publication Written informed consent was obtained from the patient's legal guardian(s) for the publication of this case report and any accompanying anonymized images. Competing interests The authors have no relevant financial or non-financial interests to disclose. Funding The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Authors' contributions LLF conceptualized the study, designed the methodology, conducted the literature review, wrote the original draft of the manuscript, and coordinated revisions. XD collected the clinical data, performed the bedside EIT monitoring, and contributed to the data interpretation and clinical correlation analysis. TYL contributed to the patient management, clinical assessment, imaging data acquisition (CT/MRI), and provided critical input on the clinical implications of the findings. YZH supervised the project, provided technical guidance on EIT data analysis and parameter calculation, reviewed and edited the manuscript, and is the corresponding author responsible for communication. All authors reviewed and approved the final version of the manuscript. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8332355","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Case Report","associatedPublications":[],"authors":[{"id":593377005,"identity":"e17635a7-ebd9-4b17-87df-ab56944f760a","order_by":0,"name":"Lingfang Liang","email":"","orcid":"","institution":"National Clinical Research Center for Child Health, Children’s Hospital of Zhejiang University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Lingfang","middleName":"","lastName":"Liang","suffix":""},{"id":593377009,"identity":"702dd1e1-ced8-4bab-b5af-7624facfb162","order_by":1,"name":"Dan xu","email":"","orcid":"","institution":"National Clinical Research Center for Child Health, Children’s Hospital of Zhejiang University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Dan","middleName":"","lastName":"xu","suffix":""},{"id":593377012,"identity":"cad4219d-2f01-49cc-91ac-c2f18fca99e9","order_by":2,"name":"Yanluan Tian","email":"","orcid":"","institution":"Tongxiang Second People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yanluan","middleName":"","lastName":"Tian","suffix":""},{"id":593377014,"identity":"a84e43d6-30b9-43db-b1c0-dd5c3454cf3b","order_by":3,"name":"Zihao Yang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIiWNgGAWjYBACfvbGhgMJP2zkGBgOALlsRGiR7Dl88MHHnjRj4rUYzEhLNpzBdjixAcwlSgtDjpk0D09a+vzGMwYMH8oOM/DPbsCvxZzhDFCLhU1uY8MZA8YZ5w4zSNw5gF+LZWMP2JbcZoYzBsy8bYcZDCQSCDjsMA9QC9vhdDaQlr9EaTnGBvZ+Ag9ICyMxWiR7mMGBbDiD4VjBwZ5z6TwSNwho4Zd/CI5KefkZhzc++FFmLcc/g4AWBJA4AI5MHmLVg+xrIEHxKBgFo2AUjCgAAGoPRoJnSHKcAAAAAElFTkSuQmCC","orcid":"","institution":"National Clinical Research Center for Child Health, Children’s Hospital of Zhejiang University School of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Zihao","middleName":"","lastName":"Yang","suffix":""}],"badges":[],"createdAt":"2025-12-11 04:08:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8332355/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8332355/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103504470,"identity":"ec052d4f-815a-4edb-9989-375895109b30","added_by":"auto","created_at":"2026-02-26 13:20:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":147445,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKey Regional Impedance Trends\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8332355/v1/ed1f3cce31da528d82e97be1.png"},{"id":103165437,"identity":"f79dc37a-0e2a-41b5-8ddb-654c0c769644","added_by":"auto","created_at":"2026-02-22 12:28:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":392648,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEIT Functional Imaging vs. MRI Structural Imaging: Comparative Schematic (August 14)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8332355/v1/51689782f5e3f510e0e92580.png"},{"id":103165439,"identity":"1a025b84-28fc-4ef5-ba3a-36d64bb8f339","added_by":"auto","created_at":"2026-02-22 12:28:11","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":394512,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGlobal and Hemispheric Electrical Impedance Curves\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8332355/v1/601910b75273515b848b39cf.png"},{"id":103504835,"identity":"7cdc01f5-b2f7-4864-a22c-b0a08e477fd9","added_by":"auto","created_at":"2026-02-26 13:21:39","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":723601,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRegional Impedance Curves\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-8332355/v1/9e49d1f7ec0b8980576698ef.png"},{"id":103509191,"identity":"bb0851e5-63ea-4567-b08d-60ca5786add0","added_by":"auto","created_at":"2026-02-26 13:57:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3345523,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8332355/v1/b8eb1b86-3b95-483c-8efb-2322ff934b89.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Application of Cerebral Electrical Impedance Tomography in Monitoring a Child with Severe Encephalitis: A Case Report and Literature Review","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSevere encephalitis is a critical pediatric condition often complicated by status epilepticus, leading to serious pathophysiological changes such as cerebral edema and intracranial hypertension. Prognosis depends on early recognition and dynamic management of brain injury. Although computed tomography (CT) and magnetic resonance imaging (MRI) remian the gold standards for assessing structural brain abnormalities, CT exposes children to ionizing radiation, and MRI requires transporting critically ill children and cannot provide real-time bedside assessment. This limitations hinder continuous monitoring with both modalities. Cerebral electrical impedance tomography (EIT) is an emerging non-invasive functional imaging technology that measures changes in the electrical conductivity of brain tissue, thereby capturing real-time alterations in intracranial fluid content and distribution associated with cerebral edema, ischemia, or hyperemia [1]. The core advantage of EIT lies in its ability to translate physiological signals into a series of quantitative parameters with clear physical and clinical significance, all derived through strict algorithms. Theoretically, EIT may detect changes in cerebral fluid dynamics earlier than the morphological changes visible on conventional imaging. This study, based on continuous EIT monitoring data from a pediatric case of severe encephalitis and with clear elaboration of parameter algorithms, systematically compares EIT findings with CT/MRI results. It aims to validate the clinical value of EIT in pediatric neurocritical care, particularly its capabilities for early warning and dynamic monitoring.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"1. Clinical Data","content":"\u003cp\u003ePatient information: A 5-year-and-10-month-old male was admitted to our Pediatric Intensive Care Unit (PICU) on August 12, 2025, presenting with \u0026quot;fever and cough for 4 days, and convulsions for 3 days.\u0026quot; The patient developed fever and cough 4 days prior to admission, followed by recurrent convulsions 1 day after the fever onset. The seizures manifested as loss of consciousness, strabismus, incontinence, and limb weakness, each episode lasting approximately 3-5 minutes and resolving spontaneously. He was initially admitted to an external hospital and received symptomatic treatments including antibiotics, antivirals, intracranial pressure reduction, and antiepileptics (midazolam, valproate, levetiracetam). However, recurrent seizures persisted, prompting transfer to our PICU. Physical examination upon admission revealed a Glasgow Coma Scale (GCS) score of 7 (E1V2M4), supple neck, positive bilateral Babinski signs, present tendon reflexes, and unremarkable cardiopulmonary and abdominal findings. Relevant laboratory tests were performed. Cerebrospinal fluid (CSF) routine, biochemistry, culture, and metagenomic next-generation sequencing (mNGS) showed no significant abnormalities. Bedside electroencephalography (EEG) indicated an interictal background with diffuse delta and theta activity. A 14-hour recording captured three seizure-onset electrographic changes originating from the left posterior head region, each lasting 4 to 6 minutes, suggesting electrographic or electroclinical seizures (ESz/ECSZ).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eImaging findings: Head CT (August 12, 2025):\u0026nbsp;No significant abnormal density foci were observed on plain scan. Midline structures were preserved.\u003c/p\u003e\n\u003cp\u003eHead MRI (August 14, 2025, see Figure 3): Multiple patchy, slightly long T1 and T2 signal foci were observed in both cerebral hemispheres, appearing slightly hyperintense on Fluid Attenuated Inversion Recovery (FLAIR), predominantly in the cortex. Some lesions showed restricted diffusion on DWI. These findings indicated multiple abnormal signals consistent with encephalitis. Continuous cerebral electrical impedance tomography (EIT) monitoring was initiated upon admission. Clinical diagnoses: (1) Severe encephalitis; (2) Status epilepticus; (3) Acute bronchopneumonia. The patient received supportive treatment for 17 days, including oxygen therapy, aggressive intracranial pressure management, intravenous immunoglobulin, high-dose steroids (methylprednisolone) for immunomodulation, and multiple antiepileptic drugs. His condition improved, with a GCS score of 13 (E4V4M5) and cessation of seizures. He was subsequently transferred to the rehabilitation ward for continued care for 24 days before discharge.\u003c/p\u003e"},{"header":"2. Monitoring methodology, data acquisition, and analysis","content":"\u003cp\u003e\u003cstrong\u003e1)\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Device and\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ed\u003c/strong\u003e\u003cstrong\u003eata\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ea\u003c/strong\u003e\u003cstrong\u003ecquisition:\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eA cerebral EIT system (model EH-300) was employed. The measurement obtained at 19:33 on August 12, 2025, was designated as the baseline. Continuous monitoring was conducted until August 15, with complete data acquired from four distinct time points. Parameters Definition and Calculation Rules: All EIT parameters were calculated according to the following formulas.[Insert Table 1 here]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Summary of cerebral impedance parameters\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameter Category\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameter Name (Abbreviation)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003eDefinition and Calculation Formula\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eClinical Significance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBaseline Impedance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGlobal Impedance\u003c/strong\u003e (GIMP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003eGIMP = (LIMP + RIMP) / 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGlobal Baseline Electrical Conductivity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBaseline Impedance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLeft/Right Hemisphere Impedance\u003c/strong\u003e (LIMP/RIMP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDevice-Measured Raw Impedance Values of Left/Right Hemisphere\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLeft/Right Hemisphere Global Fluid Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegional Impedance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLeft/Right Prefrontal\u003c/strong\u003e (L1IMP/R1IMP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDevice-Measured Impedance Values in Corresponding Regions of Interest\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrontal Cortex Fluid Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegional Impedance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLeft/Right Parietal-Basal Ganglia\u003c/strong\u003e (L2IMP/R2IMP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDevice-Measured Impedance Values in Corresponding Regions of Interest\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParietal-Basal Fluid Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegional Impedance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLeft/Right Occipito-Cerebellar\u003c/strong\u003e (L3IMP/R3IMP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDevice-Measured Impedance Values in Corresponding Regions of Interest\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOccipito-Cerebellar Fluid Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRate of Change\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDaily Rate of Change/Cumulative Rate of Change\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRate of Change = [Current Value - Previous Value (or Baseline Value)] / Previous Value (or Baseline Value) \u0026times; 100%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQuantify the temporal variation in impedance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBalance Degree\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGlobal Hemispheric Balance Degree\u003c/strong\u003e (BD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003eBD = |LIMP - RIMP| / GIMP \u0026times; 100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAssess Asymmetry Between Left and Right Cerebral Hemispheres\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBalance Degree\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnterior Balance Degree\u003c/strong\u003e (BD1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003eBD1 = |L1IMP - R1IMP| / GIMP \u0026times; 100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAssess Frontal Lobe Symmetry\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBalance Degree\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCentral Balance Degree\u003c/strong\u003e (BD2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003eBD2 = |L2IMP - R2IMP| / GIMP \u0026times; 100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAssess Parietal-Basal Ganglia Symmetry\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBalance Degree\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePosterior Balance Degree\u003c/strong\u003e (BD3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003eBD3 = |L3IMP - R3IMP| / GIMP \u0026times; 100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAssess Occipito-Cerebellar Symmetry\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e2) Imaging and\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ea\u003c/strong\u003e\u003cstrong\u003elert\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003er\u003c/strong\u003e\u003cstrong\u003eules\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eColor\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ec\u003c/strong\u003e\u003cstrong\u003eoding for\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ei\u003c/strong\u003e\u003cstrong\u003emaging:\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eUsing the patient\u0026apos;s initial impedance as the baseline, dynamic relative changes (\u0026Delta;Z) were displayed. \u003cstrong\u003eRed\u003c/strong\u003e indicates decreased impedance (increased conductivity, suggestive of elevated fluid content, such as edema), while b\u003cstrong\u003elue\u003c/strong\u003e indicates an increase in impedance (decreased conductivity, suggestive of reduced fluid content, such as ischemia).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAlert\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003et\u003c/strong\u003e\u003cstrong\u003ehresholds (\u003c/strong\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003cstrong\u003eediatric):\u003c/strong\u003e Based on pediatric reference standards, a change rate \u0026ge; \u003cstrong\u003e|\u0026plusmn;15%|\u003c/strong\u003e triggers a \u003cstrong\u003eyellow alert\u003c/strong\u003e (Level Ⅰ, requiring attention), and a change rate \u0026ge; \u003cstrong\u003e|\u0026plusmn;20%|\u003c/strong\u003e triggers a \u003cstrong\u003ered alert\u003c/strong\u003e (Level Ⅱ, indicating high risk and prompting immediate imaging reassessment).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3) Imaging\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ec\u003c/strong\u003e\u003cstrong\u003eorrelation\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ea\u003c/strong\u003e\u003cstrong\u003enalysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTemporal correlation:\u0026nbsp;The EIT monitoring timeline was aligned with the timestamps of CT and MRI examinations to analyze EIT trends before and after each imaging study.\u003c/p\u003e\n\u003cp\u003eSpatial localization correlation:\u0026nbsp;Anatomical correlation was performed by comparing regions showing abnormal impedance changes on EIT with areas of abnormal signals on MRI.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4) Results:\u0026nbsp;\u003c/strong\u003ePresentation of EIT dynamic monitoring data and calculated parameters.[Insert Table 2 here]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Cerebral EIT Continuous Monitoring Data (Derived from Calculation Formulas): Longitudinal Record with Optimal Signal Points\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"116%\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTime Point\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eGIMP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003eLIMP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003eRIMP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eL1IMP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eL2IMP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eL3IMP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eR1IMP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eR2IMP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eR3IMP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eBD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003eBD1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003eBD2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003eBD3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e08/12/2025 19:33:04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e11.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e11.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e11.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e13.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e10.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e11.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e11.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e11.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e10.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e1.96%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e15.30%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e-15.90%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e6.58%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e08/13/2025 12:54:24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e11.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e11.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e11.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e11.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e10.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e12.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e10.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e12.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e12.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-6.04%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e13.96%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e22.94%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e4.61%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e08/14/2025 17:44:13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e11.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e11.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e11.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e11.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e9.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e12.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e9.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e15.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e12.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-7.10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e13.38%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e-53.22%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e-3.04%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e08/15/2025 16:40:59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e11.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e11.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e11.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e11.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e10.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e13.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e9.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e11.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e13.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e1.80%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e17.42%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e-8.15%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e Values for BD, BD1, BD2, and BD3 were recalculated according to the aforementioned formulas. Minor discrepancies from the raw data exist due to rounding of the original measurements. This table presents the recalculated values to demonstrate the accuracy of the calculation rules. For instance:BD1(8/12) = ⎜13.13-11.41⎟ / 11.25 * 100% = 1.72 / 11.25 * 100% \u0026asymp; 15.29%.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Longitudinal Impedance Rate of Change Calculations (Derived from the Rate of Change Formula): Baseline Impedance and Rate of Change Data\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eImpedance Type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e08/12\u003cstrong\u003eBaseline Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e08/13\u003cstrong\u003eBaseline Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e08/13\u003cstrong\u003eDaily Rate of Change\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e08/14\u003cstrong\u003eBaseline Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e08/14\u003cstrong\u003eDaily Rate of Change\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e08/15\u003cstrong\u003eBaseline Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e08/15\u003cstrong\u003eDaily Rate of Change\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCumulative Rate of Change\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(08/12\u0026rarr;08/15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003eGIMP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e11.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e11.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e+3.02%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e11.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e-3.97%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e11.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e+4.67%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e+3.56%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003eLIMP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e11.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e11.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e-1.06%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e11.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.98%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e11.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e+5.66%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e+3.52%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003eRIMP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e11.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e11.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e+7.18%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e11.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.17%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e11.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e-3.10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e+3.68%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003eL1IMP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e13.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e11.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e-9.67%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e11.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e-3.29%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e11.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e-2.53%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e-14.85%▼\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003eL2IMP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e10.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e10.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.88%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e9.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e-6.83%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e10.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e+9.67%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e+1.28%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003eL3IMP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e11.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e12.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e+10.87%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e12.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e+1.14%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e13.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e+12.52%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e+26.18%▲\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003eR1IMP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e11.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e10.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e-10.25%▼\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e9.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e-2.54%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e9.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e-8.32%▼\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e-19.81%▼\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003eR2IMP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e11.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e12.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e+6.51%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e15.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e+20.14%▲\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e11.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e-26.48%▼\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e-5.93%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003eR3IMP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e10.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e12.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e+23.79%▲\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e12.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e+0.08%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e13.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e+9.17%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e+35.24%▲\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e ▼ indicates a significant decrease (rate of change \u0026le; -5%); ▲ indicates a significant increase (rate of change \u0026ge; +5%). Values in \u003cstrong\u003ered font\u003c/strong\u003e exceed the pediatric red alert threshold (\u0026ge; \u003cstrong\u003e|\u0026plusmn;20%|\u003c/strong\u003e). All rates of change were calculated using the formula:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e(Current Value - Previous Value) / Previous Value \u0026times; 100%. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eTemporal Correlation Between EIT Monitoring and Imaging Findings\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003eDate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eImaging Findings\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCore EIT Abnormalities and Alert Levels\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eComparative Analysis and Clinical Significance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e08/12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCT: \u0026quot;No significant intracranial abnormalities.\u0026quot;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrefrontal Asymmetry:\u003c/strong\u003e BD1 (15.30%) was significantly elevated.\u003cbr\u003e\u003cstrong\u003eCalculation Basis:\u003c/strong\u003e BD1 = ⎜L1IMP - R1IMP⎟ / GIMP = ⎜13.13-11.41⎟ / 11.25 \u0026asymp; 15.30%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEarly Warning:\u003c/strong\u003e EIT detected abnormalities in prefrontal fluid distribution through functional parameters (Balance Degree) when CT showed no structural abnormalities, demonstrating its capability for pre-symptomatic alert.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e08/13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo Imaging\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003cstrong\u003ePrefrontal Lobe:\u003c/strong\u003e R1IMP Daily Rate of Change: -10.25% (yellow alert)\u003cbr\u003e\u003cstrong\u003eOccipito-Cerebellar:\u003c/strong\u003e R3IMP Daily Rate of Change: +23.79% (red alert)\u003cbr\u003e\u003cstrong\u003eCalculation Basis:\u003c/strong\u003e\u003cbr\u003e\u003cimg width=\"51\" height=\"13\" src=\"https://myfiles.space/user_files/58895_8739fc6c57c1c19a/58895_custom_files/img1771512067.gif\" alt=\"image\"\u003e/11.41\u0026times;100%=-10.25% \u003cimg width=\"52\" height=\"13\" src=\"https://myfiles.space/user_files/58895_8739fc6c57c1c19a/58895_custom_files/img1771512067.gif\" alt=\"image\"\u003e/10.30\u0026times;100%=+23.79%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDynamic Deterioration:\u003c/strong\u003e EIT monitoring captured significant functional deterioration across multiple brain regions, providing a strong indication for subsequent MRI examination the following day.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e08/14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eMRI： \u003cstrong\u003e\u0026quot;Multiple abnormal signals in both cerebral hemispheres.\u0026quot;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCentral Region:\u003c/strong\u003e R2IMP Daily Rate of Change: +20.14% (red alert)\u003cbr\u003e\u003cstrong\u003eBD2 Extreme Value:\u003c/strong\u003e -53.22%\u003cbr\u003e\u003cstrong\u003eCalculation Basis:\u003c/strong\u003eBD2=⎜L2IMP-R2IMP⎟/GIMP=⎜9.41-15.33⎟/11.13\u0026asymp;-53.22%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpatial Validation:\u003c/strong\u003e The abnormal signals identified on MRI showed a high degree of spatial concordance with the regions of significant impedance change and balance disturbance localized by EIT.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e08/15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo Imaging\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCentral Region:\u003c/strong\u003e R2IMP Daily Rate of Change: -26.48% (red alert)\u003cbr\u003e\u003cstrong\u003eCalculation Basis:\u003c/strong\u003e\u003cimg width=\"51\" height=\"13\" src=\"https://myfiles.space/user_files/58895_8739fc6c57c1c19a/58895_custom_files/img1771512068.gif\" alt=\"image\"\u003e/15.33\u0026times;100%=-26.48%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePathological Evolution:\u003c/strong\u003e EIT captured a reversal in R2IMP from a sharp increase to a decrease, revealing a dynamic pathological process undetectable by conventional imaging.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e The temporal correlation analysis between EIT and the imaging gold standard demonstrates the clinical value of EIT, as the timing of EIT parameter changes shows a clear relationship with the emergence of imaging findings.\u003c/p\u003e\n\u003cp\u003eKey parameter trends and functional imaging visualization\u003c/p\u003e\n\u003cp\u003eFigure 1 shows the key regional impedance trends. [Insert Figure 1 here]\u003c/p\u003e\n\u003cp\u003eFigure 2\u0026nbsp;provides a comparative schematic of EIT functional imaging and MRI structural imaging.\u0026nbsp;[Insert Figure 2 here]\u003c/p\u003e\n\u003cp\u003eThe regional impedance curves are displayed in\u0026nbsp;Figure 4.\u0026nbsp;[Insert Figure 4 here]\u003c/p\u003e\n\u003cp\u003e5). Signal quality control and baseline strategy analysis:\u003c/p\u003e\n\u003cp\u003eWe fully recognize the challenges associated with long-term EIT monitoring in the PICU environment, including artifacts introduced by patient position changes, clinical procedures (such as suctioning), and electrode reapplication. To maximize data reliability, the following measures were implemented: a.Stabilization procedures: All electrodes were applied by a trained specialist using uniformly standardized electrodes and conductive gel, and were secured with an elastic headband to minimize displacement. Data acquisition was performed during periods when the patient was relatively stable and no intensive clinical procedures were underway. b. Signal quality assessment: The device incorporates a built-in Signal Quality Index (SQI). We exclusively acquired and analyzed Only data points with SQI values above a preset threshold (80%) were included in the analysis to exclude low-quality signals caused by poor contact or significant body movement. c. Strategy for managing electrode reapplication: Electrodes reapplication after the patient returned from CT/MRI examinations introduced baseline drift. To address this, we adopted a dual-baseline analysis strategy: Absolute baseline (Initial value from August 12): Used to assess the overall trend from admission (Cumulative rate of change). Relative/segmented baseline: For data following electrode reapplication (e.g., measurements on August 13, 14), the daily rate of change was calculated using the preceding measurement as the reference (e.g., the August 13 value as the baseline for August 14). This differential approach, based on adjacent time points, effectively mitigates systematic drift caused by absolute changes in electrode position and provided a more sensitive reflection of the true pathophysiological dynamics occurring between the two measurement periods.\u003c/p\u003e"},{"header":"3. Discussion","content":"\u003cp\u003eElectrical impedance tomography (EIT) is a non-invasive, radiation-free functional imaging technique that estimates the spatial distribution of tissue resistivity within the body by measuring transfer impedance between surface electrodes [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Different tissues exhibit distinct impedance levels, which vary under physiological and pathological conditions. Consequently, EIT can detect impedance changes resulting from such physiological and pathological shifts [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. EIT image reconstruction methods primarily include static and dynamic modes. Static reconstruction aims to recover the absolute distribution of electrical conductivity within tissues, whereas dynamic reconstruction utilizes measurement data from different time points and employs difference imaging algorithms to reconstruct relative images of conductivity change. In dynamic reconstruction, differential processing effectively mitigates model errors, thereby significantly reducing their impact on the system's imaging accuracy [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. This advantage makes dynamic reconstruction particularly valuable for bedside monitoring. EIT technology has been investigated in various clinical contexts, including pulmonary ventilation and perfusion imaging [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], brain functional imaging [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], and abdominal organ functional imaging [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Its applications extend to conditions such as breast cancer, acute respiratory distress syndrome, and stroke [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Furthermore, EIT utilizes low-frequency alternating currents (typically 1 kHz \u0026ndash; 1 MHz) that safely penetrate human tissues without ionizing effects, ensuring good biosafety.\u003c/p\u003e \u003cp\u003eThe significance of applying EIT to cerebral studies stems from the high mortality rate associated with brain injuries and their severe impact on quality of life. Its imaging mechanism relies on the principle that neuronal depolarization alters the electrical conductivity of the extracellular environment through the opening and closing of ion channels. EIT captures these bioelectrical impedance signals to generate functional imaging of cerebral activity [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. An inherent electrical property difference exists between brain tissue and blood, primarily manifested as higher electrical conductivity (i.e., lower electrical impedance) in blood. Under pathological conditions such as epileptic seizures, accompanied by neuronal hyperactivity and potential cellular edema, local cerebral blood volume undergoes significant changes. This, in turn, alters the overall electrical impedance characteristics of the cerebral cortex [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. A key advantage of EIT lies in its ability to provide a means for long-term, repeatable monitoring of these neuronal activity-related impedance fluctuations, effectively avoiding interference from artifacts such as motion [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, significant electrical conductivity heterogeneity exists among intracranial tissues. Cerebrospinal fluid exhibits high conductivity (low resistance), whereas the skull bone shows extremely low conductivity (high resistance). This complex electrical environment poses considerably greater technical challenges for cerebral EIT imaging compared with its application in other organ systems [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCurrently, cerebral EIT monitoring is primarily applied in conditions such as stroke, epilepsy, and cerebral edema. Normal, ischemic, and hemorrhagic brain tissues exhibit distinguishable electrical property differences on electrical impedance spectra [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Based on this principle, Holder et al. investigated the potential of multi-frequency EIT for the diagnosis and differential diagnosis of stroke. Their studies not only identified optimized modes suitable for stroke imaging but also confirmed the feasibility of using this technology for early stroke diagnosis, imaging assessment, and even guiding thrombolytic therapy [\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In epilepsy applications, Holder et al. reported pioneering work using EIT for functional neural activity imaging. Through subdural electrode recordings, they successfully localized epileptogenic foci, demonstrating the potential of EIT in this field [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The present case primarily focused on assessing cerebral edema associated with encephalitis. Fu et al. were the first to report that EIT could be used for real-time, non-invasive, dynamic monitoring of focal cerebral edema during clinical dehydration therapy [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Regarding fundamental research, the Song team systematically monitored cerebral electrical impedance and morphological changes across different phases following ischemic brain injury in a rat model of cerebral edema. Their study found a significant increase in brain tissue resistivity at 6 hours post-injury, which subsequently decreased between 6 and 24 hours. This temporal sequence of impedance changes confirms the potential of EIT for continuously tracking the evolution of cerebral edema [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eConventionally, clinical assessment of brain injury primarily relies on techniques such as CT, MRI, and PET. However, both CT and PET involve ionizing radiation during imaging, while MRI has limitations including longer scanning times and higher demands on patient cooperation. Furthermore, such equipment is typically expensive, has poor hardware mobility, and offers limited flexibility for repeated examinations. In contrast, EIT offers advantages such as lower cost, high reproducibility, ease of operation, and minimal adverse effects. In recent years, with continuous technological refinement and device updates, EIT has gradually been applied to the early diagnosis and personalized treatment of pediatric-related diseases.\u003c/p\u003e \u003cp\u003eThe parameter system of EIT, grounded in well-defined calculation rules, confers significant early warning value. First, in this case, EIT triggered an alert based on an elevated prefrontal balance degree (BD1) despite a negative CT scan on August 12. Balance degree indices like BD1, computed using simple relative difference formulas, effectively eliminate interference from individual baseline impedance variations and amplify localized asymmetric signals. This demonstrates EIT's sensitivity to subtle changes in the cerebral microenvironment, allowing functional monitoring to bridge the \"temporal blind zone\" between physiological abnormality and the manifestation of morphological changes. Second, the regional impedance rates of change and balance degree indices derived from calculations showed high spatial concordance with MRI findings. The \"multiple abnormal signals in both cerebral hemispheres\" confirmed by MRI corresponded well with the abnormal regions identified by EIT calculations, including decreased prefrontal impedance, sharply fluctuating parietal-basal ganglia impedance, and elevated occipito-cerebellar impedance. This indicates that EIT parameters, based on explicit ROIs and calculation formulas, can not only provide early warning of global abnormalities but also accurately localize them to specific functional areas within the cerebral hemispheres. Furthermore, EIT provides an irreplaceable dynamic monitoring perspective through continuous calculation of rates of change. In this case, the substantial bidirectional fluctuations of the right parietal-basal ganglia impedance (R2IMP), calculated according to the rate of change formula, vividly illustrated the complexity of local pathological processes in encephalitis. This real-time, continuously computed \"pathophysiological movie,\" based on strict mathematical calculations, cannot be provided by other intermittent imaging modalities.\u003c/p\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eIn this case, continuous bedside cerebral EIT monitoring, leveraging its parameter system with well-defined calculation rules (including baseline impedance, rate of change, and balance degree indices), proved to be an effective and reliable bedside functional monitoring tool. By quantifying these parameters, EIT demonstrated the ability to detect early abnormalities in cerebral functional status prior to their manifestation on CT, while also showing good concordance with structural lesions identified by MRI. Its core value lies in translating complex cerebral fluid dynamics into quantifiable, dynamic trend data, thereby enabling more timely and precise personalized treatment. As a novel functional imaging modality, cerebral EIT shows considerable potential for real-time, non-invasive monitoring of children with severe intracranial infections or brain injury in the PICU. It can assist clinicians in promptly adjusting treatment strategies and potentially improving patient outcomes. This technique is non-invasive and free from ionizing radiation. Although its application has long been primarily confined to research settings, recent clinical practice increasingly supports its practical value. The potential of EIT in broader clinical scenarios is now being actively explored. With continued validation and integration into clinical workflows, cerebral EIT is expected to become an important adjunctive tool in critical care medicine [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Hospital Ethics Management Committee (Approval No: 2025-TEC-0014). Written informed consent was obtained from the patient\u0026apos;s legal guardian(s) for the patient\u0026apos;s participation in the monitoring and for the analysis/publication of the anonymized data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWritten informed consent was obtained from the patient\u0026apos;s legal guardian(s) for the publication of this case report and any accompanying anonymized images.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLLF conceptualized the study, designed the methodology, conducted the literature review, wrote the original draft of the manuscript, and coordinated revisions.\u003c/p\u003e\n\u003cp\u003eXD collected the clinical data, performed the bedside EIT monitoring, and contributed to the data interpretation and clinical correlation analysis.\u003c/p\u003e\n\u003cp\u003eTYL contributed to the patient management, clinical assessment, imaging data acquisition (CT/MRI), and provided critical input on the clinical implications of the findings.\u003c/p\u003e\n\u003cp\u003eYZH supervised the project, provided technical guidance on EIT data analysis and parameter calculation, reviewed and edited the manuscript, and is the corresponding author responsible for communication.\u003c/p\u003e\n\u003cp\u003eAll authors reviewed and approved the final version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eTidswell A T , Gibson A , Bayford R H ,et al.Electrical impedance tomography of human brain activity with a\\ntwo-dimensional ring of scalp electrodes[J].Physiological measurement, 2001(22-1).\u003c/li\u003e\n \u003cli\u003eKe X, Hou W, Huang Q, et al. Advances in electrical impedance tomography-based brain imaging[J]. Mil Med Res, 2022,9:705-726. doi:10.1186/s40779-022-00370-7.\u003c/li\u003e\n \u003cli\u003eMurai T, Kagawa Y. Electrical impedance computed tomography based on a finite element model[J]. IEEE T Bio-med Eng, 1985, 32:177-184. doi:10.1109/TBME.1985.325526.\u003c/li\u003e\n \u003cli\u003ePettigrew RI, Peterson KP, Heetderks W, Seto B. The National Institute of Biomedical Imaging and Bioengineering marks its first five years. Acad Radiol. 2007;14:1448\u0026ndash;54.\u003c/li\u003e\n \u003cli\u003e\u0026nbsp;Frerichs I, Amato MB, van Kaam AH, Tingay DG, Zhao Z, Grychtol B, Bodenstein M, Gagnon H, B\u0026ouml;hm SH, Teschner E, Stenqvist O, Mauri T, Torsani V, Camporota L, Schibler A, Wolf GK,Gommers D, Leonhardt S, Adler A. Chest electrical impedance tomography examination, data analysis,terminology, clinical use and recommendations: consensus statement of the translational EIT development study group. Thorax. 2017;72:83\u0026ndash;93.\u003c/li\u003e\n \u003cli\u003e\u0026nbsp;Sadleir RJ, Fox RA. Detection and quantification of intraperitoneal fluid using electrical impedance tomography. IEEE Trans Biomed Eng. 2001;48:484\u0026ndash;91.\u003c/li\u003e\n \u003cli\u003eHalter RJ,Hartov A, Paulsen KD. A broadband high-frequency electrical impedance tomography system for breast imaging[J]. IEEE T BiomedEng,2008,55: 650-659.doi:10.1109/TBME.2007.903516.\u003c/li\u003e\n \u003cli\u003e\u0026nbsp;Zhao Z, Chang M, Chang M, et al. Positive endexpiratory pressure titration with electrical impedance tomography and pressure-volume curve in severe acute respiratory distress syndrome[J]. Ann Intensive Care, 2019,9:7. doi:10.1186/s13613-019-0484-0.\u003c/li\u003e\n \u003cli\u003eCao L, Li H, Fu D, et al. Real-time imaging of infarction deterioration after ischemic stroke in rats using electrical impedance tomography[J]. Physiol Meas, 2020,41:15004. doi:10.1088/1361-6579/ab69ba.\u003c/li\u003e\n \u003cli\u003eGilad O , Holder D S .Impedance changes recorded with scalp electrodes during visual evoked responses: Implications for Electrical Impedance Tomography of fast neural activity[J].Neuroimage, 2009, 47(2):514-522.DOI:10.1016/j.neuroimage.2009.04.085.\u003c/li\u003e\n \u003cli\u003eGilad O, Ghosh A, Oh D, et al. A method for recording resistance changes non-invasively during neuronal depolarization with a view to imaging brain activity with electrical impedance tomography[J].J Neurosci Meth, 2009,180:87-96. doi:10.1016/j.jneumeth.2009.03.012.\u003c/li\u003e\n \u003cli\u003eTang T , Oh S , Sadleir R J .A Robust Current Pattern for the Detection of Intraventricular Hemorrhage in Neonates Using Electrical Impedance Tomography[J].Annals of Biomedical Engineering, 2010, 38(8):2733.DOI:10.1007/s10439-010-0003-9.\u003c/li\u003e\n \u003cli\u003eBateman R M , Sharpe M D , Jagger J E ,et al.Erratum to: 36th International Symposium on Intensive Care and Emergency Medicine: Brussels, Belgium. 15-18 March 2016[J].Critical Care, 2016.DOI:10.1186/s13054-016-1358-6.\u003c/li\u003e\n \u003cli\u003eSeo JK, Lee J, Kim SW, et al. Frequency-difference electrical impedance tomography(fdEIT):algorithm development and feasibility study [J]. Physiol Meas, 2008,29:929-944. doi:10.1088/0967-3334/29/8/006.\u003c/li\u003e\n \u003cli\u003eRomsauerova A , Mcewan A , Holder D S .Identification of a suitable current waveform for acute stroke imaging[J].Physiological Measurement, 2006, 27(5).DOI:10.1088/0967-3334/27/5/S18.\u003c/li\u003e\n \u003cli\u003eMalone E, Jehl M,Arridge S, et al. Stroke type differentiation using spectrally constrained multifrequency EIT: evaluation of feasibility in a realistic head model [J]. Physiol Meas, 2014,35:1051-1066. doi:10.1088/0967-3334/35/6/1051.\u003c/li\u003e\n \u003cli\u003eMalone E , Santos G S D , Holder D ,et al.Multifrequency Electrical Impedance Tomography Using Spectral Constraints[J].IEEE Transactions on Medical Imaging, 2014, 33(2):340-350.DOI:10.1109/TMI.2013.2284966.\u003c/li\u003e\n \u003cli\u003eVongerichten AN, Santos GSD, Aristovich K, et al. Characterisation and imaging of cortical impedance changes during interictal and ictal activity in the anaesthetised rat [J]. Neuro Image, 2016, 124:813-823. doi:10.1016/j.neuroimage.2015.09.015.\u003c/li\u003e\n \u003cli\u003eFu F , Li B , Dai M ,et al.Use of Electrical Impedance Tomography to Monitor Regional Cerebral Edema during Clinical Dehydration Treatment[J].Plos One, 2014, 9(12):e113202.DOI:10.1371/journal.pone.0113202.\u003c/li\u003e\n \u003cli\u003eJiali S , Rongqing C , Lin Y ,et al.Electrical Impedance Changes at Different Phases of Cerebral Edema in Rats with Ischemic Brain Injury[J].Biomed Research International, 2018, 2018:9765174.DOI:10.1155/2018/9765174.\u003c/li\u003e\n \u003cli\u003eRUBIN J,BERRA L. Electrical impedance tomography in the adult intensive care unit:clinical applications and future directions[ J] . Current Opinion in Critical Care, 2022,28(3) :292-301.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-pediatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bped","sideBox":"Learn more about [BMC Pediatrics](http://bmcpediatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bped/default.aspx","title":"BMC Pediatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Cerebral electrical impedance tomography, Severe encephalitis, Calculation rules, Magnetic resonance imaging, Bedside monitoring, Pediatric","lastPublishedDoi":"10.21203/rs.3.rs-8332355/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8332355/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective:\u003c/strong\u003e To investigate the utility of cerebral electrical impedance tomography (EIT) in monitoring cerebral fluid dynamics in pediatric severe encephalitis, with validation against conventional neuroimaging imaging (CT/MRI).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: We present a case of a child with severe encephalitis and status epilepticus. Bedside EIT (EH-300 system) monitoring was conducted continuously for four days. Quantitative EIT parameters including whole-brain impedance, regional impedance, and balance degree index were analyzed. Temporal and spatial variations in these parameters were compared with findings from cranial CT and MRI.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: \u0026nbsp;Initial cranial CT revealed no definite structural abnormalities. In contrast, EIT detected early pathophysiological alterations, demonstrating significant prefrontal impedance asymmetry (anteroposterior balance degree BD1: 15.30%) and a progressive decrease in right prefrontal impedance (total R1IMP change rate: -19.81%). Subsequent MRI confirmed multiple abnormal signals in both cerebral hemispheres; the lesion distribution showed high spatial consistency with the regional abnormalities earlier identified by EIT. Furthermore, EIT captured dynamic fluctuations that were not assessable through real-time conventional imaging.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: Employing well-defined quantitative algorithms, cerebral EIT can identify early functional abnormalities prior to the detection of structural changes on conventional imaging. Its dynamic monitoring capability provides a valuable bedside tool for early warning, lesion localization, and therapeutic response assessment in severe encephalitis.\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Hospital Ethics Management Committee (Approval No: 2025-TEC-0014). Written informed consent was obtained from the patient’s legal guardian(s) for publication of this case report and any accompanying images.\u003c/p\u003e","manuscriptTitle":"Application of Cerebral Electrical Impedance Tomography in Monitoring a Child with Severe Encephalitis: A Case Report and Literature Review","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-22 12:28:06","doi":"10.21203/rs.3.rs-8332355/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-02-23T07:02:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"209238946833303199653097372211993138282","date":"2026-02-23T01:13:09+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-13T14:00:48+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-24T06:13:32+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-24T02:58:49+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-24T02:58:19+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pediatrics","date":"2025-12-11T04:00:02+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pediatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bped","sideBox":"Learn more about [BMC Pediatrics](http://bmcpediatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bped/default.aspx","title":"BMC Pediatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"44481665-8856-4f71-8b22-b0b096e30200","owner":[],"postedDate":"February 22nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-22T12:28:07+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-22 12:28:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8332355","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8332355","identity":"rs-8332355","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}