Electrical Impedance Tomography Data Research Articles

Noise-based correction for electrical impedance tomography.

Noisy measurements frequently cause noisy and inaccurate images in impedance imaging. No post-processing technique exists to calculate the propagation of measurement noise and use this to suppress noise in the image. The objectives were (1) to develop a post-processing method for noise-based correction (NBC) in impedance tomography, (2) to test whether NBC improves image quality in electrical impedance tomography (EIT), (3) to determine whether it is preferable to use correlated or uncorrelated noise for NBC, (4) to test whether NBC improves image quality with in vivo data and (5) to test whether NBC is stable across model and perturbation geometries. EIT was performed in silico in a 2D homogeneous circular domain and a realistic, heterogeneous 3D human head domain for four perturbations and 25 noise levels. This was validated by performing EIT for four perturbations in a circular, saline tank in 2D and a human head-shaped saline tank with a realistic skull-like layer in 3D. Images were assessed on the weighted spatial variance (WSV) with respect to the true, target image. The effect of NBC was also tested for in vivo EIT data of lung ventilation in a human thorax and cortical activity in a rat brain. On visual inspection, NBC maintained or increased image quality for all perturbations and noise levels in 2D and 3D, both experimentally and in silico. Analysis of the WSV showed that NBC significantly improved the WSV in nearly all cases. When the WSV was inferior with NBC, this was either visually imperceptible or a transformation between noisy reconstructions. For in vivo data, NBC improved image quality in all cases and preserved the expected shape of the reconstructed perturbation. In practice, uncorrelated NBC performed better than correlated NBC and is recommended as a general-use post-processing technique in EIT.

Lung Volume and Ventilation Distribution After Bariatric Surgery-High-Flow Nasal Cannula Versus CPAP.

Patients with obesity are at increased risk of postoperative pulmonary complications. CPAP has been used successfully to prevent and treat acute respiratory failure, but in many clinical scenarios, high-flow nasal cannula (HFNC) therapy is emerging as a possible alternative. We aimed to compare HFNC and CPAP in a sequential study measuring their effects on gas exchange, lung volumes, and gas distribution within the lungs measured through electrical impedance tomography (EIT). We enrolled 15 subjects undergoing laparoscopic bariatric surgery. Postoperatively they underwent the following oxygen therapy protocol (10 min/step): baseline air-entrainment mask, HFNC at increasing (40, 60, 80, and 100 L/min) and decreasing flows (80, 60, and 40 L/min), washout air-entrainment mask and CPAP (10 cm H2O). Primary outcome was the change in end-expiratory lung impedance (ΔEELI) measured by EIT data processing. Secondary outcomes were changes of global inhomogeneity (GI) index and tidal impedance variation (TIV) measured by EIT, arterial oxygenation, carbon dioxide content, pH, respiratory frequency, and subject's comfort. Thirteen subjects completed the study. Compared to baseline, ΔEELI was higher during 10 cm H2O CPAP (P = .001) and HFNC 100 L/min (P = .02), as well as during decreasing flows HFNC 80, 60, and 40 L/min (P = .008, .004, and .02, respectively). GI index was lower during HFNC 100 compared to HFNC 60increasing (P = .044), HFNC 60decreasing (P = .02) HFNC 40decreasing (P = .01), and during 10 cm H2O CPAP compared to washout period (P = .01) and HFNC 40decreasing (P = .03). TIV was higher during 10 cm H2O CPAP compared to baseline (P = .008). Compared to baseline, breathing frequency was lower at HFNC 60increasing, HFNC 100, and HFNC 80decreasing (P = .01, .02, and .03, respectively). No differences were detected regarding arterial oxygenation, carbon dioxide content, pH, and subject's comfort. HFNC at a flow of 100 L/min induced postoperative pulmonary recruitment in bariatric subjects, with no significant differences compared to 10 cm H2O CPAP in terms of lung recruitment and ventilation distribution.

Improved filtering methods to suppress cardiovascular contamination in electrical impedance tomography recordings

Objective. Electrical impedance tomography (EIT) produces clinical useful visualization of the distribution of ventilation inside the lungs. The accuracy of EIT-derived parameters can be compromised by the cardiovascular signal. Removal of these artefacts is challenging due to spectral overlapping of the ventilatory and cardiovascular signal components and their time-varying frequencies. We designed and evaluated advanced filtering techniques and hypothesized that these would outperform traditional low-pass filters. Approach. Three filter techniques were developed and compared against traditional low-pass filtering: multiple digital notch filtering (MDN), empirical mode decomposition (EMD) and the maximal overlap discrete wavelet transform (MODWT). The performance of the filtering techniques was evaluated (1) in the time domain (2) in the frequency domain (3) by visual inspection. We evaluated the performance using simulated contaminated EIT data and data from 15 adult and neonatal intensive care unit patients. Main result. Each filter technique exhibited varying degrees of effectiveness and limitations. Quality measures in the time domain showed the best performance for MDN filtering. The signal to noise ratio was best for DLP, but at the cost of a high relative and removal error. MDN outbalanced the performance resulting in a good SNR with a low relative and removal error. MDN, EMD and MODWT performed similar in the frequency domain and were successful in removing the high frequency components of the data. Significance. Advanced filtering techniques have benefits compared to traditional filters but are not always better. MDN filtering outperformed EMD and MODWT regarding quality measures in the time domain. This study emphasizes the need for careful consideration when choosing a filtering approach, depending on the dataset and the clinical/research question.

The role of electrical impedance tomography in predicting the failure of non-invasive ventilation in patients with COVID-19-associated hypoxemic acute respiratory failure: a prospective observational study

INTRODUCTION: Data on prediction of the failure of non-invasive lung ventilation (NIV) in patients with respiratory failure associated with COVID-19, based on electrical impedance tomography (EIT) patterns are limited. OBJECTIVE: To identify predictors of non-invasive lung ventilation failure in patients with respiratory insufficiency associated with COVID-19, using electrical impedance tomography data. MATERIALS AND METHODS: A monocenter prospective cohort observational study was conducted in patients with moderate-to-severe COVID-19-associated acute respiratory failure, who underwent NIV in the ICU (n = 43). EIT was used to measure the actual ventilation area (SVENT), hyperinflation area (SHYPER), ventilation delay zone (SRVD), and calculate the proportion of ventilated lungs (AVENT), proportion of hyperinflation area (ZHYPER), proportion of RVD area (ZRVD), as well as the duration of hyperinflation during one respiratory cycle (THYPER), and the ratio of hyperinflation time to inhalation time. RESULTS: The study included 43 patients admitted 15 (10–22) days after the onset of COVID-19. Patients with NIV failure (n = 34) had higher ZHYPER values on the first day (19.5 (16.3–30.5) in the NIV success group and 35.2 (25.0–45.0) in the failure group, AUROC 0.80, p = 0.004, Cut-off 39.7, Se 85 %, Sp 89 %) and the last day (20.6 (10.4–28.5) in the success group and 32.7 (26.4–43.3) in the failure group, AUROC 0.92, p = 0.003, Cut-off 32.7, Se 50 %, Sp 100 %), as well as a higher THYPER/TINSP ratio on the last day of NIV (37.5 (31.0–47.9) in the success group and 65.3 (43.7–88.4) in the failure group, AUROC 0.87, p = 0.001, Cut-off 52.7, Se 71 %, Sp 100 %). AVENT, ZRVD, and inhalation time did not show prognostic significance. CONCLUSIONS: EIT can predict the NIV failure in moderate-to-severe ARDS associated with COVID-19, based on the assessment of alveolar overdistention zones. Further research are needed to investigate this theory.

A Blockchain-Based Secure Sharing Scheme for Electrical Impedance Tomography Data

Real-time electrical impedance tomography (EIT) data sharing is becoming increasingly necessary, due to the extensive use of EIT technology in various sectors, including material analysis, biomedicine, and industrial process monitoring. The prevalence of portable EIT equipment and remote imaging technology has led to a predominance of centralized storage, Internet protocol transmission, and certificates from certificate authorities (CA) in telemedicine data. This has resulted in compromised data security, network communication delays, high CA maintenance costs, increased risks of medical data privacy breaches, and low security. Therefore, this paper offers a consortia blockchain-based method for exchanging EIT data that addresses security and integrity concerns during data storage and exchange, while maintaining transparency and traceability. Proprietary re-encryption techniques are employed to guarantee traceability when exchanging anonymous data, enabling precise control over data access. This scheme serves to protect both data and identity privacy, as well as to trace the actual identity of potential malicious users, while also thwarting any coordinated efforts between partially trusted parties and data requesters seeking unauthorized access to confidential information. Additionally, a combination of blockchain and InterPlanetary File System (IPFS) distributed storage technology is utilized to ease the burden of EIT data storage. The feasibility and effectiveness of the proposed solution were validated through a series of experiments, demonstrating its ability to effectively prevent data tampering and misuse, reduce data management costs, and enhance the efficiency and quality of data sharing.

Electrical impedance tomography in anaesthetised chickens (Gallus domesticus).

The applicability of electrical impedance tomography (EIT) in birds is unknown. This study aimed to evaluate the use of EIT in anaesthetised chickens in four recumbency positions. Four adult Hyline chickens were anaesthetised with isoflurane in oxygen, and intubated endotracheally for computed tomography (CT). A rubber belt was placed around the coelom caudal to the shoulder joint. A chicken-specific finite element (FE) model, which is essential to generate anatomically accurate functional EIT images for analysis, was constructed based on the CT images obtained at the belt level. Ten additional chickens were anaesthetised with the same protocol. An EIT electrode belt was placed at the same location. The chickens were breathing spontaneously and positioned in dorsal, ventral, right and left lateral recumbency in a randomised order. For each recumbency, raw EIT data were collected over 2 min after 13 min of stabilisation. The data were reconstructed into functional EIT images. EIT variables including tidal impedance variation (TIV), centre of ventilation right to left (CoVRL) and ventral to dorsal (CoVVD), right to left (RL) ratio, impedance change (ΔZ) and eight regional impedance changes including the dorsal, central-dorsal, central-ventral and ventral regions of the right and left regions were analysed. Four breathing patterns (BrP) were observed and categorised based on the expiratory curve. A linear mixed model was used to compare EIT variables between recumbencies. Fisher's exact test was used to compare the frequencies of breathing patterns for each recumbency. The ΔZ observed was synchronous to ventilation, and represented tidal volume of the cranial air sacs as confirmed by CT. Significant differences were found in CoVVD and regional impedance changes between dorsal and ventral recumbencies (P < 0.05), and in CoVRL, RL ratio and regional impedance changes between right and left recumbencies (P < 0.05), which suggested a tendency for the distribution of ventilation to shift towards non-dependent air sacs. No differences were found for TIV and respiratory rate between recumbencies. Recumbency had a significant effect on the frequencies of each of the four BrPs (P = 0.001). EIT can monitor the magnitude and distribution of ventilation of the cranial air sacs in different recumbencies in anaesthetised chickens.

Validation of three-dimensional thoracic electrical impedance tomography of horses during normal and increased tidal volumes

Objective. Data from two-plane electrical impedance tomography (EIT) can be reconstructed into various slices of functional lung images, allowing for more complete visualisation and assessment of lung physiology in health and disease. The aim of this study was to confirm the ability of 3D EIT to visualise normal lung anatomy and physiology at rest and during increased ventilation (represented by rebreathing). Approach. Two-plane EIT data, using two electrode planes 20 cm apart, were collected in 20 standing sedate horses at baseline (resting) conditions, and during rebreathing. EIT data were reconstructed into 3D EIT whereby tidal impedance variation (TIV), ventilated area, and right-left and ventral-dorsal centres of ventilation (CoVRL and CoVVD, respectively) were calculated in cranial, middle and caudal slices of lung, from data collected using the two planes of electrodes. Main results. There was a significant interaction of time and slice for TIV (p < 0.0001) with TIV increasing during rebreathing in both caudal and middle slices. The ratio of right to left ventilated area was higher in the cranial slice, in comparison to the caudal slice (p = 0.0002). There were significant effects of time and slice on CoVVD whereby the cranial slice was more ventrally distributed than the caudal slice (p < 0.0009 for the interaction). Significance. The distribution of ventilation in the three slices corresponds with topographical anatomy of the equine lung. This study confirms that 3D EIT can accurately represent lung anatomy and changes in ventilation distribution during rebreathing in standing sedate horses.

Distribution of regional lung function in upright healthy subjects determined by electrical impedance tomography in two chest examination planes.

Objective. The variation in pulmonary gas content induced by ventilation is not uniformly distributed in the lungs. The aim of our study was to characterize the differences in spatial distribution of ventilation in two transverse sections of the chest using electrical impedance tomography (EIT).Approach. Twenty adult never-smokers, 10 women and 10 men (mean age ± SD, 31 ± 9 years), were examined in a sitting position with the EIT electrodes placed consecutively in a caudal (6th intercostal space) and a cranial (4th intercostal space) chest location. EIT data were acquired during quiet breathing, slow and forced full expiration manoeuvres. Impedance variations representing tidal volume (VT), vital capacity (VC), forced expiratory volume in 1 s (FEV1) and forced vital capacity (FVC) were calculated at the level of individual image pixels and their spatial distribution was determined using the following EIT measures: the centres of ventilation in ventrodorsal (CoVvd) and right-to-left direction (CoVrl), the dorsal and right fractions of ventilation, the coefficient of variation (CV) and the global inhomogeneity (GI) index.Main results. The sums of pixel ventilation-related impedance variations reproduced reliably the volumetric dissimilarities amongVT, VC, FEV1and FVC, with no significant differences noted between the two examination planes. Significant differences in ventilation distribution were found between the planes during tidal breathing and slow full expiration, mainly regarding the ventrodorsal direction, with higher values of CoVvdand dorsal fraction of ventilation in the caudal plane (p< 0.01). No significant differences in the spatial distribution of FEV1and FVC were detected between the examination planes.Significance. The spatial distribution of ventilation differed between the two examination planes only during the relaxed (quiet breathing and slow VC manoeuvre) but not during the forced ventilation. This effect is attributable to the differences in thoracoabdominal mechanics between these types of ventilation.

Optimization of lung ventilation and perfusion in anesthetized horses using a ventilation mode with flow-limited expiration.

To investigate the mechanisms underlying the improved arterial oxygenation described with flow-limited expiration (FLEX) ventilation in anesthetized horses. 5 healthy adult research horses. Horses underwent volume-controlled ventilation for 60 minutes (VCV1), followed by 60 minutes of FLEX, and 30 minutes of VCV (VCV2). Main outcomes included the arterial partial pressure of oxygen-to-Fio2 (PF) ratio and electrical impedance tomography (EIT)-derived functional indices at the end of each phase. The EIT data were used to create regional maps of relative lung ventilation and perfusion as well as regional maps of ventilation/perfusion (V/Q) ratios. Ventilation indices derived from EIT included the fraction of expired volume in 1 second (FEV1; %) and the time it took for the EIT signal to drop to 50% of the peak signal at end-inspiration (TClose50; seconds). Data were analyzed with 2-way ANOVA for repeated measures. P < .05 was considered significant. The PF ratio increased significantly with FLEX compared to both VCV1 and VCV2 (P < .01). There were no differences in the relative distribution of ventilation nor perfusion between ventilation strategies. However, when ventilation and perfusion were superimposed and V/Q ratio maps were constructed, FLEX had a homogenizing effect toward values of 1.0. The FEV1 was shorter (P < .01) and the TClose50 was longer (P < .001) in all regions during FLEX compared to both VCV1 and VCV2. Our findings suggest that FLEX ventilation in anesthetized horses enhances regional V/Q matching, likely by prolonging expiratory aeration and reducing airway closure.

Applied machine learning for stroke differentiation by electrical impedance tomography with realistic numerical models

Electrical impedance tomography (EIT) may have potential to overcome existing limitations in stroke differentiation, enabling low-cost, rapid, and mobile data collection. Combining bioimpedance measurement technologies such as EIT with machine learning classifiers to support decision-making can avoid commonly faced reconstruction challenges due to the nonlinear and ill-posed nature of EIT imaging. Therefore, in this work, we advance this field through a study integrating realistic head models with clinically relevant test scenarios, and a robust architecture consisting of nested cross-validation and principal component analysis. Specifically, realistic head models are designed which incorporate the highly conductive layers of cerebrospinal fluid in the subarachnoid space and ventricles. In total, 135 unique models are created to represent a large patient population, with normal, haemorrhagic, and ischemic brains. Simulated EIT voltage data generated from these models are used to assess the classification performance of support vector machines. Parameters explored include driving frequency, signal-to-noise ratio, kernel function, and composition of binary classes. Classifier accuracy at 60 dB signal-to-noise ratio, reported as mean and standard deviation, are (79.92% ± 10.82%) for lesion differentiation, (74.78% ± 3.79%) for lesion detection, (77.49% ± 15.90%) for bleed detection, and (60.31% ± 3.98%) for ischemia detection (after ruling out bleed). The results for each method were obtained with statistics from 3 independent runs with 17,280 observations, polynomial kernel functions, and feature reduction of 76% by PCA (from 208 to 50 features). While results of this study show promise for stroke differentiation using EIT data, our findings indicate that the achievable accuracy is highly dependent on the classification scenario and application-specific classifiers may be necessary to achieve acceptable accuracy.

Deciphering Mechanisms of Respiratory Fetal-to-Neonatal Transition in Very Preterm Infants.

Rationale: The respiratory mechanisms of a successful transition of preterm infants after birth are largely unknown. Objectives: To describe intrapulmonary gas flows during different breathing patterns directly after birth. Methods: Analysis of electrical impedance tomography data from a previous randomized trial in preterm infants at 26-32 weeks gestational age. Electrical impedance tomography data for individual breaths were extracted, and lung volumes as well as ventilation distribution were calculated for end of inspiration, end of expiratory braking and/or holding maneuver, and end of expiration. Measurements and Main Results: Overall, 10,348 breaths from 33 infants were analyzed. We identified three distinct breath types within the first 10 minutes after birth: tidal breathing (44% of all breaths; sinusoidal breathing without expiratory disruption), braking (50%; expiratory brake with a short duration), and holding (6%; expiratory brake with a long duration). Only after holding breaths did end-expiratory lung volume increase: Median (interquartile range [IQR]) = 2.0 AU/kg (0.6 to 4.3), 0.0 (-1.0 to 1.1), and 0.0 (-1.1 to 0.4), respectively; P < 0.001]. This was mediated by intrathoracic air redistribution to the left and non-gravity-dependent parts of the lung through pendelluft gas flows during braking and/or holding maneuvers. Conclusions: Respiratory transition in preterm infants is characterized by unique breathing patterns. Holding breaths contribute to early lung aeration after birth in preterm infants. This is facilitated by air redistribution during braking/holding maneuvers through pendelluft flow, which may prevent lung liquid reflux in this highly adaptive situation. This study deciphers mechanisms for a successful fetal-to-neonatal transition and increases our pathophysiological understanding of this unique moment in life. Clinical trial registered with www.clinicaltrials.gov (NCT04315636).

Ensemble deep learning model for dimensionless respiratory airflow estimation using respiratory sound

In recent years, computerized methods for analyzing respiratory function have gained increased attention within the scientific community. This study proposes a deep-learning model to estimate the dimensionless respiratory airflow using only respiratory sound without prior calibration. We developed hybrid deep learning models (CNN + LSTM) to extract features from the respiratory sound and model their temporal dependencies. Then, we used an ensemble approach to combine multiple outputs of our models and obtain the respiratory airflow waveform for entire respiratory audio signals as the final output. We conducted a comprehensive set of experiments and evaluated the models using several regression evaluation metrics to assess how the models would perform in various circumstances of different complexity. The methods were developed and evaluated considering respiratory sound and electrical impedance tomography (EIT) data from 50 respiratory patients (15 female and 35 male with an average age of 67.4 ± 8.9 years and body mass index of 27.8 ± 5.6 kg/m2). An external assessment was conducted using an external database, the Respiratory Sound Database (RSD). This was an indirect evaluation because the RSD does not provide the ground truth values of the dimensionless respiratory airflow. In the most complex evaluation task (Task II), we achieved the following results for the estimation of the normalized dimensionless respiratory airflow curve: mean absolute error = 0.134 ± 0.061; root mean squared error = 0.170 ± 0.075; dynamic time warping similarity = 3.282 ± 1.514; Pearson correlation coefficient = 0.770 ± 0.235. External assessment with the RSD showed that the performance of our model decreased when devices different from the ones used for their training were considered. Our study demonstrated that deep learning models could reliably estimate the dimensionless respiratory airflow.

Comparison of different inversion strategies for electrical impedance tomography (EIT) measurements

SUMMARY Electrical impedance tomography (EIT) is a promising method to image the frequency-dependent complex electrical conductivity distribution of the subsurface in the mHz to kHz frequency range. In contrast to the well-developed electrical resistivity tomography (ERT) method, the inversion approach for EIT data is less established. Different inversion strategies have been proposed, but the implications of the differences between these methods have not been investigated yet. In this study, we aim to compare four different inversion strategies for EIT measurements. The first strategy (CVI) formulates the inverse problem in the complex number domain and is mathematically the most elegant method. The second strategy (RVI) is the established real-valued inversion method, which decouples the inversion of the real and imaginary parts and completely ignores the complex nature. The third strategy (ALT) is very similar to the RVI strategy in case of small phase angles, but it considers the complex coupling in the forward operator and alternately updates the real and imaginary parts of the model in the case of large phase angles. The fourth and final strategy (CVI+) was newly formulated in this study. It fully considers the complex nature of EIT measurements but separates the treatment of the real and imaginary part in terms of the data weighting and regularization. The different inversion strategies were tested with two synthetic models. The first model has a small phase contrast and the second model has a large phase contrast. In the case of a small phase contrast, the CVI strategy was able to resolve the distribution of electrical conductivity amplitude, but the inversion result for the phase angle was less reliable. The other three strategies presented similar results and the models were well resolved within the expected data misfit. In the case of a model with large phase contrast, only the newly formulated CVI + strategy was able to produce reliable results. It was found that the extremely large phase angle can have a significant influence on the modelled amplitude of data. The cross-sensitivity (i.e. the imaginary part of the sensitivity) that describes the influence on the real part of data due to a change in the imaginary part of model, or that on the imaginary part of data due to a change in the real part of model, provided unique information during the inversion. It was concluded that the CVI + strategy is theoretically the most comprehensive and correct approach for EIT inversion, but that in the case of small phase angles the RVI strategy has the practical advantage that no complex calculations are required, which substantially reduces the required computational effort.

BRACETS: Bimodal repository of auscultation coupled with electrical impedance thoracic signals

Background and Objective: Respiratory diseases are among the most significant causes of morbidity and mortality worldwide, causing substantial strain on society and health systems. Over the last few decades, there has been increasing interest in the automatic analysis of respiratory sounds and electrical impedance tomography (EIT). Nevertheless, no publicly available databases with both respiratory sound and EIT data are available. Methods: In this work, we have assembled the first open-access bimodal database focusing on the differential diagnosis of respiratory diseases (BRACETS: Bimodal Repository of Auscultation Coupled with Electrical Impedance Thoracic Signals). It includes simultaneous recordings of single and multi-channel respiratory sounds and EIT. Furthermore, we have proposed several machine learning-based baseline systems for automatically classifying respiratory diseases in six distinct evaluation tasks using respiratory sound and EIT (A1, A2, A3, B1, B2, B3). These tasks included classifying respiratory diseases at sample and subject levels. The performance of the classification models was evaluated using a 5-fold cross-validation scheme (with subject isolation between folds). Results: The resulting database consists of 1097 respiratory sounds and 795 EIT recordings acquired from 78 adult subjects in two countries (Portugal and Greece). In the task of automatically classifying respiratory diseases, the baseline classification models have achieved the following average balanced accuracy: Task A1 - 77.9±13.1%; Task A2 - 51.6±9.7%; Task A3 - 38.6±13.1%; Task B1 - 90.0±22.4%; Task B2 - 61.4±11.8%; Task B3 - 50.8±10.6%. Conclusion: The creation of this database and its public release will aid the research community in developing automated methodologies to assess and monitor respiratory function, and it might serve as a benchmark in the field of digital medicine for managing respiratory diseases. Moreover, it could pave the way for creating multi-modal robust approaches for that same purpose.

The influence of gravity on electrical impedance tomography measurements during upper body position change

ObjectiveThe aim of the study was to examine the influence of gravity on regional ventilation measured by electrical impedance tomography (EIT) with the standard electrode belt position at the 5th intercostal space during tilting from supine to sitting positions. MethodsA total of 30 healthy volunteers were examined prospectively in supine position during quiet tidal breathing. Subsequently, the bed was tilted so that the upper body of the subjects achieved 30, 60 and 90° every 3 min. Regional ventilation distribution and end-expiratory lung impedance (EELI) were monitored with EIT throughout the whole experiment. Absolute tidal volumes were measured with spirometry and the volume-impedance ratio was calculated for each position. ResultsThe volume-impedance ratio did not differ statistically between the studied body positions but 11 subjects exhibited a large change in ratio at one of the positions (outside 99.3% coverage). In general, ventilation distribution became more heterogeneous and moved towards dorsal regions as the upper body was tilted to 90-degree position. EELI increased and tidal volume decreased. The lung regions identified at various positions differed significantly. ConclusionGravity has non-negligible influence on EIT data, as the upper body tilted from supine to sitting positions. The standard electrode belt position might be reconsidered if ventilation distribution is to be compared between supine and sitting positions.

Machine learning-based signal quality assessment for cardiac volume monitoring in electrical impedance tomography

Owing to recent advances in thoracic electrical impedance tomography (EIT), a patient’s hemodynamic function can be noninvasively and continuously estimated in real-time by surveilling a cardiac volume signal (CVS) associated with stroke volume and cardiac output. In clinical applications, however, a CVS is often of low quality, mainly because of the patient’s deliberate movements or inevitable motions during clinical interventions. This study aims to develop a signal quality indexing method that assesses the influence of motion artifacts on transient CVSs. The assessment is performed on each cardiac cycle to take advantage of the periodicity and regularity in cardiac volume changes. Time intervals are identified using the synchronized electrocardiography system. We apply divergent machine-learning methods, which can be sorted into discriminative-model and manifold-learning approaches. The use of machine-learning could be suitable for our real-time monitoring application that requires fast inference and automation as well as high accuracy. In the clinical environment, the proposed method can be utilized to provide immediate warnings so that clinicians can minimize confusion regarding patients’ conditions, reduce clinical resource utilization, and improve the confidence level of the monitoring system. Numerous experiments using actual EIT data validate the capability of CVSs degraded by motion artifacts to be accurately and automatically assessed in real-time by machine learning. The best model achieved an accuracy of 0.95, positive and negative predictive values of 0.96 and 0.86, sensitivity of 0.98, specificity of 0.77, and AUC of 0.96.

Impact of sedation, body position change and continuous positive airway pressure on distribution of ventilation in healthy foals.

This study aimed to compare the distribution of ventilation measured by electrical impedance tomography (EIT), in foals under varying clinical conditions of sedation, postural changes, and continuous positive airway pressure (CPAP). To support the interpretation of EIT variables, specific spirometry data and F-shunt calculation were also assessed. Six healthy Thoroughbred foals were recruited for this sequential experimental study. EIT and spirometry data was recorded: (1) before and after diazepam-sedation, (2) after moving from standing to right lateral recumbency, (3) in dorsal recumbency during no CPAP (CPAP0) and increasing levels of CPAP of 4, 7, and 10 cmH2O (CPAP4, 7, 10, respectively). Ventral to dorsal (COVVD) and right to left (COVRL) center of ventilation, silent spaces, tidal impedance variation, regional ventilation distribution variables and right to left lung ventilation ratio (R:L) were extracted. Minute ventilation was calculated from tidal volume (VT) and respiratory rate. F-Shunt was calculated from results of arterial blood gas analysis. Statistical analysis was performed using linear mixed effects models (significance determined at p < 0.05). (1) Respiratory rate was lower after sedation (p = 0.0004). (2) In right lateral recumbency (compared to standing), the COVVD (p = 0.0012), COVRL (p = 0.0057), left centro-dorsal (p = 0.0071) and dorsal (p < 0.0001) regional ventilation were higher, while the right ventral (p = 0.0016) and dorsal (p = 0.0145) regional ventilation, and R:L (p = 0.0017) were lower. (3) Data of two foals for CPAP10 was excluded from statistical analysis due to prolonged apnea. Stepwise increase of CPAP led to increases of COVVD (p = 0.0028) and VT (p = 0.0011). A reduction of respiratory rate was detected with increasing CPAP levels (p < 0.0001). (1) In healthy foals, diazepam administration did not alter distribution of ventilation or minute ventilation, (2) lateral recumbency results in collapse of dependent areas of the lung, and (3) the use of CPAP in dorsal recumbency at increasing pressures improves ventilation in dependent regions, suggesting improvement of ventilation-perfusion mismatch.

Ventilation during continuous compressions or at 30:2 compression-to-ventilation ratio results in similar arterial oxygen and carbon dioxide levels in an experimental model of prolonged cardiac arrest

BackgroundIn refractory out-of-hospital cardiac arrest, transportation to hospital with continuous chest compressions (CCC) from a chest compression device and ventilation with 100% oxygen through an advanced airway is common practice. Despite this, many patients are hypoxic and hypercapnic on arrival, possibly related to suboptimal ventilation due to the counterpressure caused by the CCC. We hypothesized that a compression/ventilation ratio of 30:2 would provide better ventilation and gas exchange compared to asynchronous CCC during prolonged experimental cardiopulmonary resuscitation (CPR).MethodsWe randomized 30 anaesthetized domestic swine (weight approximately 50 kg) with electrically induced ventricular fibrillation to the CCC or 30:2 group and bag-valve ventilation with a fraction of inspired oxygen (FiO2) of 100%. We started CPR after a 5-min no-flow period and continued until 40 min from the induction of ventricular fibrillation. Chest compressions were performed with a Stryker Medical LUCAS® 2 mechanical chest compression device. We collected arterial blood gas samples every 5 min during the CPR, measured ventilation distribution during the CPR using electrical impedance tomography (EIT) and analysed post-mortem computed tomography (CT) scans for differences in lung aeration status.ResultsThe median (interquartile range [IQR]) partial pressure of oxygen (PaO2) at 30 min was 110 (52–117) mmHg for the 30:2 group and 70 (40–171) mmHg for the CCC group. The median (IQR) partial pressure of carbon dioxide (PaCO2) at 30 min was 70 (45–85) mmHg for the 30:2 group and 68 (42–84) mmHg for the CCC group. No statistically significant differences between the groups in PaO2 (p = 0.40), PaCO2 (p = 0.79), lactate (p = 0.37), mean arterial pressure (MAP) (p = 0.47) or EtCO2 (p = 0.19) analysed with a linear mixed model were found. We found a deteriorating trend in PaO2, EtCO2 and MAP and rising PaCO2 and lactate levels through the intervention. There were no differences between the groups in the distribution of ventilation in the EIT data or the post-mortem CT findings.ConclusionsThe 30:2 and CCC protocols resulted in similar gas exchange and lung pathology in an experimental prolonged mechanical CPR model.

Benefits of secretion clearance with high frequency percussive ventilation in tracheostomized critically ill patients: a pilot study

Clearance of secretions remains a challenge in ventilated patients. Despite high-frequency percussive ventilation (HFPV) showing benefits in patients with cystic fibrosis and neuromuscular disorders, very little is known about its effects on other patient categories. Therefore, we designed a physiological pilot study investigating the effects on lung aeration and gas exchange of short HFPV cycles in tracheostomized patients undergoing mechanical ventilation. Electrical impedance tomography (EIT) was recorded at baseline (T0) by a belt wrapped around the patient's chest, followed by the HFPV cycle lasting 10 min. EIT data was collected again after the HFPV cycle (T1) as well as after 1 h (T2) and 3 h (T3) from T0. Variation from baseline of end-expiratory lung impedance (∆EELI), tidal variation (TIV) and global inhomogeneity index (GI) were computed. Arterial blood was also taken for gas analysis. HFPV cycle significantly improved the ∆EELI at T1, T2 and T3 when compared to baseline (p < 0.05 for all comparisons). The ratio between arterial partial pressure and inspired fraction of oxygen (PaO2/FiO2) also increased after the treatment (p < 0.001 for all comparison) whereas TIV (p = 0.132) and GI (p = 0.114) remained unchanged. Short cycles of HFPV superimposed to mechanical ventilation promoted alveolar recruitment, as suggested by improved ∆EELI, and improved oxygenation in tracheostomized patients with high load of secretion.Trial Registration Prospectively registered on www.clinicaltrials.gov (NCT05200507; dated 6th January 2022).

Relationship between lung ultrasound and electrical impedance tomography as regional assessment tools during PEEP titration in acute respiratory distress syndrome caused by multi-lobar pneumonia: a pilot study.

Acute respiratory distress syndrome (ARDS) caused by multilobar pneumonia (MLP) is markedly different from typical ARDS in pathology, imaging characteristics, and lung mechanics. Regional lung assessment is required. We aimed to analyze the relationship between two regional assessment tools, lung ultrasound (LUS) and electrical impedance tomography (EIT) during positive end-expiratory pressure (PEEP) titration, and determine an appropriate PEEP level. We conducted a prospective study of patients with ARDS caused by MLP with PaO2/FiO2 < 150mmHg. All subjects were equipped with two EIT belts connected with a single EIT machine to measure upper and lower hemithorax impedance change alternatingly at each PEEP level. LUS score was simultaneously determined in chest wall regions corresponding to the EIT regions during PEEP titration. We acquired EIT and LUS data in eight regions of interest at seven PEEP levels in 12 subjects. Therefore, 672 pairs of data were obtained for analysis. There were significant relationships between LUS score and tidal impedance variation and pixel compliance (Cpix). The Spearman's rho between LUS score vs. tidal impedance variation and LUS score vs. the Cpix were -0.142, P < 0.001, and -0.195, P < 0.001, respectively. The relationship between the LUS score and Cpix remained the same at every PEEP level but did not reach statistical significance. The individual's mean expected PEEP by LUS was similar to the EIT [10.33(± 1.67) vs. 10.33(± 1.44) cm H2O, P = 0.15]. Regarding the MLP, the LUS scores were associated with EIT parameters, and LUS scores might proof helpful for finding individual PEEP settings in MLP.