In electrical impedance tomography (EIT), it is difficult to obtain the intracranial impedance due to the highly resistive skull enclosing the brain. Therefore, a high-precision data acquisition system is required for brain EIT. In this paper, we used a high-precision digital synthesis method and a digital demodulation technique with high noise immunity to eliminate random errors. Moreover, we focused on two problems encountered during EIT data acquisition: 1) the shunt effect on the excitation current due to the distributed capacitance between electrodes and ground and 2) high common-mode voltages in the boundary measurements. We designed a new electrode interface to reduce the influence of the distributed capacitance and a programmable current source to accurately compensate for the excited current. We also proposed a new voltmeter circuit with improved CMRR. Overall, this EIT data acquisition system can produce a programmable current with SNR greater than 89 dB. It can also measure the voltage difference precisely with CMRR higher than 75 dB with a 1-kQ impedance imbalance. The results on a calibration model show that this system has a high SNR of 83 dB and a low reciprocity error of 0.125%. In addition, EIT imaging results were acquired using a brain physical phantom. The system can detect small disturbances of 0.35% in volume (1.99% of the cross-sectional area) and 17% in resistivity. Experiments on healthy volunteers also suggest that small intracranial impedance variations due to temporary occlusion and reperfusion of the unilateral carotid artery may be monitored by the system.

The rapid printing of 3D parts with desired electrical properties enables numerous applications. Fused deposition modeling (FDM) using conductive thermoplastic composites has been a valuable approach for such fabrication. The parts produced by FDM possess various controllable structural features, but the effects of the structural features on the electrical properties remain to be determined. This study investigated the effects of these features on the electrical resistivity and resistivity anisotropy of 3D-printed ABS/CB composites. The effects of the process parameters of FDM, including the layer thickness, raster width, and air gap, on the resistivity in both the vertical and horizontal directions for cubic samples were studied because the internal structure of the printed parts depended on those process parameters. The resistivities of printed parts in different parameter combinations were measured by an impedance analyzer and finite element models were created to investigate the relationship between the resistivity and the internal structure. The results indicated that the parameters remarkably affected the resistivity due to the influence of voids and the bonding condition between adjacent fibers. The resistivity in the vertical direction ranged from 70.40 ± 2.88 Ω·m to 180.33 ± 8.21 Ω·m, and the resistivity in the horizontal direction ranged from 41.91 ± 2.29 Ω·m to 58.35 ± 0.61 Ω·m at the frequency of 1 kHz. Moreover, by adjusting the resistivities in different directions, the resistivity anisotropy of the printed parts can be manipulated from 1.01 to 3.59. This research may serve as a reference to fabricate parts with sophisticated geometry with desired electrical resistivity and resistivity anisotropy.

Objective . The aim of the study was to compare titration of positive end-expiratory pressure (PEEP) with electrical impedance tomography (EIT) and with ventilator-embedded pressure–volume (PV) loop in moderate to severe acute respiratory distress syndrome (ARDS). Approach . Eighty-seven moderate to severe ARDS patients (arterial oxygen partial pressure to fractional inspired oxygen ratio, PaO ₂ /FiO ₂ ≤ 200 mmHg) were randomized to either EIT group ( n = 42) or PV group ( n = 45). All patients received identical medical care using the same general support guidelines and protective mechanical ventilation. In the EIT group, the selected PEEP equaled the airway pressure at the intercept between cumulated collapse and overdistension percentages curves and in the PV group, at the pressure where maximal hysteresis was reached. Main results . Baseline characteristics and settings were comparable between the groups. After optimization, PEEP was significantly higher in the PV group (17.4 ± 1.7 versus 16.2 ± 2.6 cmH ₂ O, PV versus EIT groups, p = 0.02). After 48 h, driving pressure was significantly higher in the PV group (12.4 ± 3.6 versus 10.9 ± 2.5 cmH ₂ O, p = 0.04). Lung mechanics and oxygenation were better in the EIT group but did not statistically differ between the groups. The survival rate was lower in the PV group (44.4% versus 69.0%, p = 0.02; hazard ratio 2.1, confidence interval 1·1–3.9). None of the other pre-specified exploratory clinical endpoints were significantly different. Significance . In moderate to severe ARDS, PEEP titration guided with EIT, compared with PV curve, might be associated with improved driving pressure and survival rate. Trial registration: NCT03112512, 13 April, 2017.

Proper monitoring of anesthesia stages can guarantee the safe performance of clinical surgeries. In this study, different anesthesia stages were classified using near-infrared spectroscopy (NIRS) signals with machine learning. The cerebral hemodynamic variables of right proximal oxyhemoglobin (HbO ₂ ) in maintenance (MNT), emergence (EM) and the consciousness (CON) stage were collected and then the differences between the three stages were compared by phase-amplitude coupling (PAC). Then combined with time-domain including linear (mean, standard deviation, max, min and range), nonlinear (sample entropy) and power in frequency-domain signal features, feature selection was performed and finally classification was performed by support vector machine (SVM) classifier. The results show that the PAC of the NIRS signal was gradually enhanced with the deepening of anesthesia level. A good three-classification accuracy of 69.27% was obtained, which exceeded the result of classification of any single category feature. These results indicate the fesibility of NIRS signals in performing three or even more anesthesia stage classifications, providing insight into the development of new anesthesia monitoring modalities.

Variations of conductive fluid content in brain tissue (e.g. cerebral edema) change tissue impedance and can potentially be measured by Electrical Impedance Tomography (EIT), an emerging medical imaging technique. The objective of this work is to establish the feasibility of using EIT as an imaging tool for monitoring brain fluid content. a prospective study. In this study EIT was used, for the first time, to monitor variations in cerebral fluid content in a clinical model with patients undergoing clinical dehydration treatment. The EIT system was developed in house and its imaging sensitivity and spatial resolution were evaluated on a saline-filled tank. 23 patients with brain edema. The patients were continuously imaged by EIT for two hours after initiation of dehydration treatment using 0.5 g/kg intravenous infusion of mannitol for 20 minutes. Overall impedance across the brain increased significantly before and after mannitol dehydration treatment (p = 0.0027). Of the all 23 patients, 14 showed high-level impedance increase and maintained this around 4 hours after the dehydration treatment whereas the other 9 also showed great impedance gain during the treatment but it gradually decreased after the treatment. Further analysis of the regions of interest in the EIT images revealed that diseased regions, identified on corresponding CT images, showed significantly less impedance changes than normal regions during the monitoring period, indicating variations in different patients' responses to such treatment. EIT shows potential promise as an imaging tool for real-time and non-invasive monitoring of brain edema patients.

Cerebral edema after brain injury can lead to brain damage and death if diagnosis and treatment are delayed. This study investigates the feasibility of employing electrical impedance tomography (EIT) as a non-invasive imaging tool for monitoring the development of cerebral edema, in which impedance imaging of the brain related to brain water content is compared with intracranial pressure (ICP). We enrolled forty patients with cerebral hemorrhage who underwent lateral external ventricular drain with intraventricular ICP and EIT monitoring for 3 h after initiation of dehydration treatment. The average reconstructed impedance value (ARV) calculated from EIT images was compared with ICP. Dehydration effects induced changes in ARV and ICP showed a close negative correlation in all patients, and the mean correlation reached R2 = 0.78 ± 0.16 (p < .001). A regression equation (R2 = 0.62, p < .001) was formulated from the total of measurement data. The 95% limits of agreement were - 6.13 to 6.13 mmHg. Adaptive clustering and variance analysis of normalized changes in ARV and ICP showed 92.5% similarity and no statistically significant differences (p > .05). Moreover, the sensitivity, specificity and area under the curve of changes in ICP >10 mmHg were 0.65, 0.73 and 0.70 respectively. The findings show that EIT can monitor changes in brain water content associated with cerebral edema, which could provide a real-time and non-invasive imaging tool for early identification of cerebral edema and the evaluation of mannitol dehydration.