Separation of ventilation and perfusion of electrical impedance tomography image streams using multi-dimensional ensemble empirical mode decomposition.

Objective:In the future, thoracic Electrical Impedance Tomography (EIT) monitoring may include continuous and simultaneous tracking of both breathing and heart activity. However, an effective way to decompose an EIT image stream into physiological processes as a ventilation-related and cardiac-related signal is missing. Approach:This study analyses the potential of Multi-dimensional Ensemble Empirical Mode Decomposition (MEEMD) by application of the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and a novel frequency-based combination criterion for de-trending, denoising and source separation of EIT image streams, collected from 9 healthy male test subjects with similar age and constitution. Main results:In this paper, a novel approach to estimate the lung, the heart and the perfused regions of an EIT image is proposed, which is based on the Root Mean Square Error (RMSE) between the index of maximal respiratory and cardiac variation to their surroundings. The summation of the indexes of the respective regions reveals physiologically meaningful time signals, separated into the physiological bandwidths of ventilation and heart activity at rest. Moreover, the respective regions were compared with the relative thorax movement and photoplethysmogram (PPG) signal. In linear regression analysis and in the Bland-Altman plot, the beat-to-beat time course of both the ventilation-related signal and the cardiac-related signal showed a high similarity with the respective reference signal. Significance:Analysis of the data reveals a fair separation of ventilatory and cardiac activity realizing the aimed source separation, with optional de-trending and noise-removal. For all analyses, a feasible correlation of 0.587 to 0.905 was found between the cardiac-related and the PPG signal. .

Breath-by-breath comparison of a novel percutaneous phrenic nerve stimulation approach with mechanical ventilation in juvenile pigs: a pilot study.

About one in three critically ill patients requires mechanical ventilation (MV). Prolonged MV, however, results in diaphragmatic weakness, which itself is associated with delayed weaning and increased mortality. Inducing active diaphragmatic contraction via electrical phrenic nerve stimulation (PNS) not only provides the potential to reduce diaphragmatic muscular atrophy but also generates physiological-like ventilation and therefore offers a promising alternative to MV. Reasons why PNS is not yet used in critical care medicine are high procedural invasiveness, insufficient evidence, and lack of side-by-side comparison to MV. This study aims to establish a minimal-invasive percutaneous, bilateral electrode placement approach for sole PNS breathing and thereby enable, for the first time, a breath-by-breath comparison to MV. Six juvenile German Landrace pigs received general anesthesia and orotracheal intubation. Following the novel ultrasound-guided, landmark-based, 4-step approach, two echogenic needles per phrenic nerve were successfully placed. Stimulation effectiveness was evaluated measuring tidal volume, diaphragmatic thickening and tomographic electrical impedance in a breath-by-breath comparison to MV. Following sufficient bilateral phrenic nerve stimulation in all pigs, PNS breaths showed a 2.2-fold increase in diaphragmatic thickening. It induced tidal volumes in the lung-protective range by negative pressure inspiration and improved dorso-caudal regional ventilation in contrast to MV. Our study demonstrated the feasibility of a novel ultrasound-guided, percutaneous phrenic nerve stimulation approach, which generated sufficient tidal volumes and showed more resemblance to physiological breathing than MV in a breath-by-breath comparison.

The gamma-variate in contrast-enhanced imaging: a unified view and method from computed to electrical impedance tomography.

Objective.Modern medical imaging plays a vital role in clinical practice, enabling non-invasive visualization of anatomical structures. Dynamic contrast enhancement (DCE) imaging is a technique that uses contrast agents to visualize blood flow dynamics in a time-resolved manner. It can be applied to different modalities, such as computed tomography (CT) and electrical impedance tomography (EIT). This study aims to develop a common theoretical and practical hemodynamic extraction basis for DCE modelling across modalities, based on the gamma-variate function.Approach.The study introduces a framework to generate time-intensity curves for multiple DCE imaging modalities from user-defined hemodynamic parameters. Thus, extensive datasets were simulated for both DCE-CT and EIT, representing different hemodynamic scenarios. Additionally, gamma-variate extensions to account for several physiological effects were detailed in a modality-agnostic manner, and three corresponding fitting strategies, namely nonlinear, linear, and a novel hybrid approach, were implemented and compared on the basis of accuracy of parameter estimation, first pass reconstruction, speed of computation, and failure rate.Main results.As a result, we found the linear method to be the most modality-dependent, exhibiting the greatest bias, variance and failure rates, although remaining the fastest alternative. The hybrid method at least matches the state-of-the-art nonlinear method's accuracy, while improving its robustness and speed by 10 times.Significance.Our research suggests that the hybrid method may bring noteworthy accuracy and efficiency improvements in handling the high-dimensionality of DCE imaging in general, being a step towards real-time processing. Moreover, our generative model presents a potential asset to produce benchmarking and data augmentation datasets across modalities.

Pulmonary CT perfusion robustly measures cardiac output in the context of multilevel pulmonary occlusion: a porcine study.

BACKGROUND: To validate pulmonary computed tomography (CT) perfusion in a porcine model by invasive monitoring of cardiac output (CO) using thermodilution method. METHODS: Animals were studied at a single center, using a Swan-Ganz catheter for invasive CO monitoring as a reference. Fifteen pigs were included. Contrast-enhanced CT perfusion of the descending aorta and right and left pulmonary artery was performed. For variation purposes, a balloon catheter was inserted to block the contralateral pulmonary vascular bed; additionally, two increased CO settings were created by intravenous administration of catecholamines. Finally, stepwise capillary occlusion was performed by intrapulmonary arterial injection of 75-µm microspheres in four stages. A semiautomatic selection of AFs and a recirculation-aware tracer-kinetics model to extract the first-pass of AFs, estimating blood flow with the Stewart-Hamilton method, was implemented. Linear mixed models (LMM) were developed to calibrate blood flow calculations accounting with individual- and cohort-level effects. RESULTS: Nine of 15 pigs had complete datasets. Strong correlations were observed between calibrated pulmonary (0.73, 95% confidence interval [CI] 0.6-0.82) and aortic blood flow measurements (0.82, 95% CI, 0.73-0.88) and the reference as well as agreements (± 2.24 L/min and ± 1.86 L/min, respectively) comparable to the state of the art, on a relatively wide range of right ventricle-CO measurements. CONCLUSIONS: CT perfusion validly measures CO using LMMs at both individual and cohort levels, as demonstrated by referencing the invasive CO. RELEVANCE STATEMENT: Possible clinical applications of CT perfusion for measuring CO could be in acute pulmonary thromboembolism or to assess right ventricular function to show impairment or mismatch to the left ventricle. KEY POINTS: • CT perfusion measures flow in vessels. • CT perfusion measures cumulative cardiac output in the aorta and pulmonary vessels. • CT perfusion validly measures CO using LMMs at both individual and cohort levels, as demonstrated by using the invasive CO as a reference standard.

A physiological model of phrenic nerve excitation by electrical stimulation.

Mechanical ventilation is essential in intensive care treatment but leads to diaphragmatic atrophy, which in turn contributes to prolonged weaning and increased mortality. One approach to prevent diaphragmatic atrophy while achieving pulmonary ventilation is electrical stimulation of the phrenic nerve. To automize phrenic nerve stimulation resulting in lung protective tidal volumes with lowest possible currents, mathematical models are required. Nerve stimulation models are often complex, so many parameters have to be identified prior to implementation. This paper presents a novel, simplified approach to model phrenic nerve excitation to obtain an individualized patient model using a few data points. The latter is based on the idea that nerve fibers are excited when the electric field exceeds a threshold. The effect of the geometry parameter on the model output was analyzed, and the model was validated with measurement data from a pig trial (RMSE in between 0.44 × 10-2and 1.64 × 10-2for parameterized models). The modeled phrenic nerve excitation behaved similarly to the measured tidal volumes, and thus could be used to develop automated phrenic nerve stimulation systems for lung protective ventilation.

Hardware-in-the-loop simulation of vascular cannula interaction.

Traditionally, the functional development, testing and verification of higher level automation, control and safety functions in extracorporeal circulations systems relies very much on the conduction of animal experiments. This especially applies to functions requiring interaction with the patients physiology. Besides ethical aspects, animal experiments have the disadvantage of higher complexity, limited reproducibility and certain pathological conditions can only be represented with difficulty. An alternative to this can be Hardware-in-the-loop (HIL) simulators.In this paper we present a HIL solution to simulate the hydraulic interaction between the cannula and the connected blood vessel. We derive a hydraulic model for an elastic vessel with particular emphasis on negative transmural pressure. In this case the resistance is mainly determined by collapse phenomena. The next section describes the design of an universal resistance actuator that can simulate vascular resistances in the expected range. Combined in the HIL simulator, the simulation model then generates the setpoint for the actuator while simultaneously receiving the resulting internal states of the hydraulic interface. This creates a truly interactive HIL simulator where the device under test interacts in the same way as with a physiological system.

A Physical Phantom for the Simulation of Neonatal Thermoregulation.

Preterm infants are at an increased health risk due to their low maturity. To monitor their health, vital signs are measured using contact-based methods. The adhesive sensors used to detect body temperature can damage the sensitive skin of neonates. Thus, a subject of current research is non-invasive measurement methods based on infrared thermography. In this context, thermal phantoms can be used to develop contactless temperature measurement systems and, furthermore, investigate the thermal behavior of preterm infants. In this work, an improved thermal phantom is introduced to simulate the thermoregulation of a premature infant. The shape and size are adapted to the body of a premature infant in the 29th week of pregnancy. The phantom consists of a 3D-printed frame to which carbon fiber heating elements and Pt1000 temperature sensors are attached. The frame is enclosed by a thermally conductive skin layer made of a silicone boron nitride mixture. Ball joints allow the body parts to tilt and rotate, enabling the phantom to model different body postures. Using PI controllers, the thermal phantom can achieve desired temperatures in 13 different areas of the body while maintaining a homogeneous temperature distribution on the skin surface. In addition, pathological temperature scenarios such as a central-peripheral temperature difference or a change in body temperature can be simulated with a maximum deviation of ± 0.4 °C.

Early Prediction of Neonatal Sepsis From Synthetic Clinical Data Using Machine Learning.

Neonatal sepsis is one of the most serious complications in neonatal intensive care units. Due to the often immature immune system, sepsis-related comorbidities are the major contributors to increased neonatal mortality. The rapid progression of the disease makes early treatment critical for patient survival. However, early diagnosis of sepsis remains difficult due to its non-specific symptoms. In recent years, Machine Learning-based techniques have been used in various medical applications to predict diseases using clinical data. In this work, we optimized and evaluated four prediction models with different architectural concepts. Two public datasets containing clinical data from adults and neonates were used for training. The adult data were collected to pre-train the models. Since neonatal data with sepsis diagnosis are very limited, we propose an augmentation method to generate synthetic clinical data. For the final evaluation, the real data of neonatal patients were defined as a test set. An AUROC of 0.91 and an AUPRC of 0.38 were obtained. These results are promising for early prediction of neonatal sepsis using artificial data for augmentation.Clinical relevance- This work demonstrates the potential of Machine Learning-based prediction models for the detection of sepsis to improve the early diagnosis of life-threatening conditions in neonatal intensive care units.

Shape analysis of training data for neural networks in Electrical Impedance Tomography.

Electrical Impedance Tomography (EIT) is a cost-effective and fast way to visualize dielectric properties of the human body, through the injection of alternating currents and measurement of the resulting potential on the bodies surface. However, this comes at the cost of low resolution as EIT is a non-linear ill-posed inverse problem. Recently Deep Learning methods have gained the interest in this field, as they provide a way to mimic non-linear functions. Most of the approaches focus on the structure of the Artificial Neural Networks (ANNs), while only glancing over the used training data. However, the structure of the training data is of great importance, as it needs to be simulated. In this work, we analyze the effect of basic shapes to be included as targets in the training data set. We compared inclusions of ellipsoids, cubes and octahedra as training data for ANNs in terms of reconstruction quality. For that, we used the well-established GREIT figures of merit on laboratory tank measurements. We found that ellipsoids resulted in the best reconstruction quality of EIT images. This shows that the choice of simulation data has an impact on the ANN reconstruction quality.Clinical relevance- This work helps to improve time independent EIT reconstruction, which in turn allows for extraction of time independent features of e.g., the lung.

Sensor Fusion of Cardiorespiratory Signals Using an Adaptive Kalman Filter.

For unobtrusive monitoring of vital signs, redundant sensors are beneficial to fuse several sensor measurements which can improve the estimation of, e.g. heart rate and respiratory rate. In this paper, an adaptive unscented Kalman filter is used to estimate respiratory rate and heart rate on a new simplified model for cardiorespiratory coupling. Additionally, the Kalman filter is tuned to incorporate the non-white system noise of the model. The Kalman filter is tested on synthesised data with variations regarding SNR, model mismatch and amount of sensors. For respiratory rate, a median squared error of as low as 0.02BPM2 and, for heart rate, a median squared error of as low as 0.2BPM2 for ideal assumptions is achieved.

Post-Mortem Extracorporeal Membrane Oxygenation Perfusion Rat Model: A Feasibility Study.

The development of biomedical soft- or hardware frequently includes testing in animals. However, large efforts have been made to reduce the number of animal experiments, according to the 3Rs principle. Simultaneously, a significant number of surplus animals are euthanized without scientific necessity. The primary aim of this study was to establish a post-mortem rat perfusion model using extracorporeal membrane oxygenation (ECMO) in surplus rat cadavers and generate first post vivo results concerning the oxygenation performance of a recently developed ECMO membrane oxygenator. Four rats were euthanized and connected post-mortem to a venous-arterial ECMO circulation for up to eight hours. Angiographic perfusion proofs, blood gas analyses and blood oxygenation calculations were performed. The mean preparation time for the ECMO system was 791 ± 29 s and sufficient organ perfusion could be maintained for 463 ± 26 min, proofed via angiographic imaging and a mean femoral arterial pressure of 43 ± 17 mmHg. A stable partial oxygen pressure, a 73% rise in arterial oxygen concentration and an exponentially increasing oxygen extraction ratio up to 4.75 times were shown. Considering the 3Rs, the established post-mortal ECMO perfusion rat model using surplus animals represents a promising alternative to models using live animals. Given the preserved organ perfusion, its use could be conceivable for various biomedical device testing.

On the cardiorespiratory coordination assessed by the photoplethysmography imaging technique.

Cardiorespiratory coordination (CRC) probes the interaction between cardiac and respiratory oscillators in which cardiac and respiratory activity are synchronized, with individual heartbeats occurring at approximately the same temporal positions during several breathing cycles. An increase of CRC has previously been related to pathological stressful states. We studied CRC employing coordigrams computed from non-contact photoplethysmography imaging (PPGI) and respiratory data using the optical flow method. In a blocked study design, we applied the cold pressure test (CPT), water at ambient temperature (AWT), and intermittent resting conditions. In controls (no intervention), CRC remained on initial low levels throughout measurements. In the experimental group (AWT and CPT intervention), CRC decreased during AWT and CPT. Following both interventions, CRC increased significantly, with a rebound effect following AWT. In controls, HR increased steadily over time. CPT evoked a significant HR increase which correlated with subjective stress/pain ratings. The CRC increase following AWT correlated significantly with subjective pain (r = .79) and stress (r = .63) ratings. Furthermore, we observed a significant correlation (r = - .80) between mean RMSSD and mean duration of CRC, which further supports an association between autonomic state and CRC level. CRC analysis obtained from cutaneous tissue perfusion data therefore appears to be a sensitive and useful method for the study of CRC and ANS activity. Future studies need to investigate the physiological principles and clinical significance of these findings.

Evaluation of electric phrenic nerve stimulation patterns for mechanical ventilation: a pilot study.

Diaphragm atrophy is a common side effect of mechanical ventilation and results in prolonged weaning. Electric phrenic nerve stimulation presents a possibility to avoid diaphragm atrophy by keeping the diaphragm conditioned in sedated patients. There is a need of further investigation on how to set stimulation parameters to achieve sufficient ventilation. A prototype system is presented with a systematic evaluation for stimulation pattern adjustments. The main indicator for efficient stimulation was the tidal volume. The evaluation was performed in two pig models. As a major finding, the results for biphasic pulses were more consistent than for alternating pulses. The tidal volume increased for a range of pulse frequency and pulse width until reaching a plateau at 80-120 Hz and 0.15 ms. Furthermore, the generated tidal volume and the stimulation pulse frequency were significantly correlated (0.42-0.84, [Formula: see text]). The results show which stimulation parameter combinations generate the highest tidal volume. We established a guideline on how to set stimulation parameters. The guideline is helpful for future clinical applications of phrenic nerve stimulation.

Automated Signal Quality Assessment of Single-Lead ECG Recordings for Early Detection of Silent Atrial Fibrillation.

Atrial fibrillation (AF) is an arrhythmic cardiac disorder with a high and increasing prevalence in aging societies, which is associated with a risk for stroke and heart failure. However, early detection of onset AF can become cumbersome since it often manifests in an asymptomatic and paroxysmal nature, also known as silent AF. Large-scale screenings can help identifying silent AF and allow for early treatment to prevent more severe implications. In this work, we present a machine learning-based algorithm for assessing signal quality of hand-held diagnostic ECG devices to prevent misclassification due to insufficient signal quality. A large-scale community pharmacy-based screening study was conducted on 7295 older subjects to investigate the performance of a single-lead ECG device to detect silent AF. Classification (normal sinus rhythm or AF) of the ECG recordings was initially performed automatically by an internal on-chip algorithm. The signal quality of each recording was assessed by clinical experts and used as a reference for the training process. Signal processing stages were explicitly adapted to the individual electrode characteristics of the ECG device since its recordings differ from conventional ECG tracings. With respect to the clinical expert ratings, the artificial intelligence-based signal quality assessment (AISQA) index yielded strong correlation of 0.75 during validation and high correlation of 0.60 during testing. Our results suggest that large-scale screenings of older subjects would greatly benefit from an automated signal quality assessment to repeat measurements if applicable, suggest additional human overread and reduce automated misclassifications.

Corrigendum: Evaluation of lung function in a German single center cohort of young patients with sickle cell disease using EIT and standard techniques.

[This corrects the article DOI: 10.3389/fmed.2023.1100180.].

Facial thermal response to non-painful stressor in premature and term neonates.

BACKGROUND: This study is a preliminary clinical investigation with the objective to evaluate the facial thermal response of premature and term neonates to a non-painful stressor (hunger) using infrared thermography (IRT). The development of objective and reliable parameters to monitor pain and stress is of relevance for optimal neonatal outcome and achieving a better management of patient comfort. METHODS: We enrolled 12 neonates ranging from 27 to 39 weeks gestation (median: 34) and aged 3-79 days (median: 13). Recordings were performed before and after feeding, with and without hunger. Six regions of interest were chosen for evaluation (nose tip, periorbital and corrugator region, forehead, perioral and chin region). RESULTS: There was an increase in the facial temperature in infants immediately prior to their next feed relative to infants who were not hungry, with the nasal tip being the facial evaluation site with the greatest temperature change. CONCLUSIONS: The IRT appears to be a feasible and suitable method to detect changes in the neonatal patient. The thermal variations observed seem to reflect an arousal mediated by the parasympathetic nervous system, which has been described in existing infant stress research. IMPACT: This is the first study to examine the use of infrared thermography (IRT) in monitoring the facial thermal response to a mild stressor (hunger) in premature and term neonates. Hunger as a mild, non-pain-associated stressor showed a significant effect on the facial temperature. The thermal signature of the regions of interest chosen showed hunger-related thermal variations. Results suggest the feasibility and suitability of IRT as an objective diagnostic tool to approach stress and changes in the condition of the neonatal patient.

SOLVe: a closed-loop system focused on protective mechanical ventilation.

BACKGROUND: Mechanical ventilation is an essential component in the treatment of patients with acute respiratory distress syndrome. Prompt adaptation of the settings of a ventilator to the variable needs of patients is essential to ensure personalised and protective ventilation. Still, it is challenging and time-consuming for the therapist at the bedside. In addition, general implementation barriers hinder the timely incorporation of new evidence from clinical studies into routine clinical practice. RESULTS: We present a system combing clinical evidence and expert knowledge within a physiological closed-loop control structure for mechanical ventilation. The system includes multiple controllers to support adequate gas exchange while adhering to multiple evidence-based components of lung protective ventilation. We performed a pilot study on three animals with an induced ARDS. The system achieved a time-in-target of over 75 % for all targets and avoided any critical phases of low oxygen saturation, despite provoked disturbances such as disconnections from the ventilator and positional changes of the subject. CONCLUSIONS: The presented system can provide personalised and lung-protective ventilation and reduce clinician workload in clinical practice.

Evaluation of Different Contrast Agents for Regional Lung Perfusion Measurement Using Electrical Impedance Tomography: An Experimental Pilot Study.

Monitoring regional blood flow distribution in the lungs appears to be useful for individually optimizing ventilation therapy. Electrical impedance tomography (EIT) can be used at the bedside for indicator-based regional lung perfusion measurement. Hypertonic saline is widely used as a contrast agent but could be problematic for clinical use due to potential side effects. In five ventilated healthy pigs, we investigated the suitability of five different injectable and clinically approved solutions as contrast agents for EIT-based lung perfusion measurement. Signal extraction success rate, signal strength, and image quality were analyzed after repeated 10 mL bolus injections during temporary apnea. The best results were obtained using NaCl 5.85% and sodium-bicarbonate 8.4% with optimal success rates (100%, each), the highest signal strengths (100 ± 25% and 64 ± 17%), and image qualities (r = 0.98 ± 0.02 and 0.95 ± 0.07). Iomeprol 400 mg/mL (non-ionic iodinated X-ray contrast medium) and Glucose 5% (non-ionic glucose solution) resulted in mostly well usable signals with above average success rates (87% and 89%), acceptable signal strength (32 ± 8% and 16 + 3%), and sufficient image qualities (r = 0.80 ± 0.19 and 0.72 ± 0.21). Isotonic balanced crystalloid solution failed due to a poor success rate (42%), low signal strength (10 ± 4%), and image quality (r = 0.43 ± 0.28). While Iomeprol might enable simultaneous EIT and X-ray measurements, glucose might help to avoid sodium and chloride overload. Further research should address optimal doses to balance reliability and potential side effects.

On quantification and maximization of information transfer in network dynamical systems.

Information flow among nodes in a complex network describes the overall cause-effect relationships among the nodes and provides a better understanding of the contributions of these nodes individually or collectively towards the underlying network dynamics. Variations in network topologies result in varying information flows among nodes. We integrate theories from information science with control network theory into a framework that enables us to quantify and control the information flows among the nodes in a complex network. The framework explicates the relationships between the network topology and the functional patterns, such as the information transfers in biological networks, information rerouting in sensor nodes, and influence patterns in social networks. We show that by designing or re-configuring the network topology, we can optimize the information transfer function between two chosen nodes. As a proof of concept, we apply our proposed methods in the context of brain networks, where we reconfigure neural circuits to optimize excitation levels among the excitatory neurons.