Fabian Braun

Cooperative dry-electrode sensors for multi-lead biopotential and bioimpedance monitoring.

Cooperative sensors is a novel measurement architecture that allows the acquiring of biopotential signals on patients in a comfortable and easy-to-integrate manner. The novel sensors are defined as cooperative in the sense that at least two of them work in concert to measure a target physiological signal, such as a multi-lead electrocardiogram or a thoracic bioimpedance.This paper starts by analysing the state-of-the-art methods to simultaneously measure biopotential and bioimpedance signals, and justifies why currently (1) passive electrodes require the use of shielded or double-shielded cables, and (2) active electrodes require the use of multi-wired cabled technologies, when aiming at high quality physiological measurements.In order to overcome the limitations of the state-of-the-art, a new method for biopotential and bioimpedance measurement using the cooperative sensor is then presented. The novel architecture allows the acquisition of the aforementioned biosignals without the need of shielded or multi-wire cables by splitting the electronics into separate electronic sensors comprising each of two electrodes, one for voltage measurement and one for current injection. The sensors are directly in contact with the skin and connected together by only one unshielded wire. This new configuration requires one power supply per sensor and all sensors need to be synchronized together to allow them to work in concert.After presenting the working principle of the cooperative sensor architecture, this paper reports first experimental results on the use of the technology when applied to measuring multi-lead ECG signals on patients. Measurements performed on a healthy patient demonstrate the feasibility of using this novel cooperative sensor architecture to measure biopotential signals and compliance with common mode rejection specification accordingly to international standard (IEC 60601-2-47) has also been assessed.By reducing the need of using complex wiring setups, and by eliminating the presence of central recording devices (cooperative sensors directly sense and store the measured biosignals on the site), the depicted novel technology is a candidate to a novel generation of highly-integrated, comfortable and reliable technologies that measure physiological signals in real-life scenarios.

A Versatile Noise Performance Metric for Electrical Impedance Tomography Algorithms

Electrical impedance tomography (EIT) is an emerging technology for real-time monitoring of patients under mechanical ventilation. EIT has the potential to offer continuous medical monitoring while being noninvasive, radiation free, and low cost. Due to their ill-posedness, image reconstruction typically uses regularization, which implies a hyperparameter controlling the tradeoff between noise rejection and resolution or other accuracies. In order to compare reconstruction algorithms, it is common to choose hyperparameter values such that the reconstructed images have equal noise performance (NP), i.e., the amount of measurement noise reflected in the images. For EIT many methods have been suggested, but none work well when the data originate from different measurement setups, such as for different electrode positions or measurement patterns. To address this issue, we propose a new NP metric based on the average signal-to-noise ratio in the image domain. The approach is validated for EIT using simulation experiments on a human thorax model and measurements on a resistor phantom. Results show that the approach is robust to the measurement configuration (i.e., number and position of electrodes, skip pattern) and the reconstruction algorithm used. We propose this novel approach as a way to select optimized measurement configurations and algorithms.

Non-invasive monitoring of pulmonary artery pressure from timing information by EIT: experimental evaluation during induced hypoxia

Monitoring of pulmonary artery pressure (PAP) in pulmonary hypertensive patients is currently limited to invasive solutions. We investigate a novel non-invasive approach for continuous monitoring of PAP, based on electrical impedance tomography (EIT), a safe, low-cost and non-invasive imaging technology. EIT recordings were performed in three healthy subjects undergoing hypoxia-induced PAP variations. The pulmonary pulse arrival time (PAT), a timing parameter physiologically linked to the PAP, was automatically calculated from the EIT signals. Values were compared to systolic PAP values from Doppler echocardiography, and yielded strong correlation scores ([Formula: see text]) for all three subjects. Results suggest the feasibility of non-invasive, unsupervised monitoring of PAP.

Limitations and challenges of EIT-based monitoring of stroke volume and pulmonary artery pressure

OBJECTIVE Electrical impedance tomography (EIT) shows potential for radiation-free and noninvasive hemodynamic monitoring. However, many factors degrade the accuracy and repeatability of these measurements. Our goal is to estimate the impact of this variability on the EIT-based monitoring of two important central hemodynamic parameters: stroke volume (SV) and pulmonary artery pressure (PAP). APPROACH We performed simulations on a 4D ([Formula: see text]) bioimpedance model of a human volunteer to study the influence of four potential confounding factors (electrode belt displacement, electrode detachment, changes in hematocrit and lung air volume) on the performance of EIT-based SV and PAP estimation. Results were used to estimate how these factors affect the EIT measures of either absolute values or relative changes (i.e. trending). MAIN RESULTS Our findings reveal that the absolute measurement of SV via EIT is very sensitive to electrode belt displacements and lung conductivity changes. Nonetheless, the trending ability of SV EIT might be a promising alternative. The timing-based measurement of PAP is more robust to lung conductivity changes but sensitive to longitudinal belt displacements at severe hypertensive levels and to rotational displacements (independent of the PAP level). SIGNIFICANCE We identify and quantify the challenges of EIT-based SV and PAP monitoring. Absolute SV via EIT is challenging, but trending is feasible, while both the absolute and trending of PAP via EIT are mostly impaired by belt displacements.

Accuracy and reliability of noninvasive stroke volume monitoring via ECG-gated 3D electrical impedance tomography in healthy volunteers

Cardiac output (CO) and stroke volume (SV) are parameters of key clinical interest. Many techniques exist to measure CO and SV, but are either invasive or insufficiently accurate in clinical settings. Electrical impedance tomography (EIT) has been suggested as a noninvasive measure of SV, but inconsistent results have been reported. Our goal is to determine the accuracy and reliability of EIT-based SV measurements, and whether advanced image reconstruction approaches can help to improve the estimates. Data were collected on ten healthy volunteers undergoing postural changes and exercise. To overcome the sensitivity to heart displacement and thorax morphology reported in previous work, we used a 3D EIT configuration with 2 planes of 16 electrodes and subject-specific reconstruction models. Various EIT-derived SV estimates were compared to reference measurements derived from the oxygen uptake. Results revealed a dramatic impact of posture on the EIT images. Therefore, the analysis was restricted to measurements in supine position under controlled conditions (low noise and stable heart and lung regions). In these measurements, amplitudes of impedance changes in the heart and lung regions could successfully be derived from EIT using ECG gating. However, despite a subject-specific calibration the heart-related estimates showed an error of 0.0 ± 15.2 mL for absolute SV estimation. For trending of relative SV changes, a concordance rate of 80.9% and an angular error of −1.0 ± 23.0° were obtained. These performances are insufficient for most clinical uses. Similar conclusions were derived from lung-related estimates. Our findings indicate that the key difficulty in EIT-based SV monitoring is that purely amplitude-based features are strongly influenced by other factors (such as posture, electrode contact impedance and lung or heart conductivity). All the data of the present study are made publicly available for further investigations.

Noninvasive pulmonary artery pressure monitoring by EIT: a model-based feasibility study.

Current monitoring modalities for patients with pulmonary hypertension (PH) are limited to invasive solutions. A novel approach for the noninvasive and unsupervised monitoring of pulmonary artery pressure (PAP) in patients with PH was proposed and investigated. The approach was based on the use of electrical impedance tomography (EIT), a noninvasive and safe monitoring technique, and was tested through simulations on a realistic 4D bio-impedance model of the human thorax. Changes in PAP were induced in the model by simulating multiple types of hypertensive conditions. A timing parameter physiologically linked to the PAP via the so-called pulse wave velocity principle was automatically estimated from the EIT data. It was found that changes in PAP could indeed be reliably monitored by EIT, irrespective of the pathophysiological condition that caused them. If confirmed clinically, these findings could open the way for a new generation of noninvasive PAP monitoring solutions for the follow-up of patients with PH.

Towards an unsupervised device for the diagnosis of childhood pneumonia in low resource settings: Automatic segmentation of respiratory sounds

Pneumonia remains the worldwide leading cause of children mortality under the age of five, with every year 1.4 million deaths. Unfortunately, in low resource settings, very limited diagnostic support aids are provided to point-of-care practitioners. Current UNICEF/WHO case management algorithm relies on the use of a chronometer to manually count breath rates on pediatric patients: there is thus a major need for more sophisticated tools to diagnose pneumonia that increase sensitivity and specificity of breath-rate-based algorithms. These tools should be low cost, and adapted to practitioners with limited training. In this work, a novel concept of unsupervised tool for the diagnosis of childhood pneumonia is presented. The concept relies on the automated analysis of respiratory sounds as recorded by a point-of-care electronic stethoscope. By identifying the presence of auscultation sounds at different chest locations, this diagnostic tool is intended to estimate a pneumonia likelihood score. After presenting the overall architecture of an algorithm to estimate pneumonia scores, the importance of a robust unsupervised method to identify inspiratory and expiratory phases of a respiratory cycle is highlighted. Based on data from an on-going study involving pediatric pneumonia patients, a first algorithm to segment respiratory sounds is suggested. The unsupervised algorithm relies on a Mel-frequency filter bank, a two-step Gaussian Mixture Model (GMM) description of data, and a final Hidden Markov Model (HMM) interpretation of inspiratory-expiratory sequences. Finally, illustrative results on first recruited patients are provided. The presented algorithm opens the doors to a new family of unsupervised respiratory sound analyzers that could improve future versions of case management algorithms for the diagnosis of pneumonia in low-resources settings.

Modelling of C/Cl isotopic behaviour during chloroethene biotic reductive dechlorination: Capabilities and limitations of simplified and comprehensive models

Predicting the fate of chloroethenes in groundwater is essential when evaluating remediation strategies. Such predictions are expected to be more accurate when incorporating isotopic parameters. Although secondary chlorine isotope effects have been observed during reductive dechlorination of chloroethenes, development of modelling frameworks and simulation has thus far been limited. We have developed a novel mathematical framework to simulate the C/Cl isotopic fractionation during reductive dechlorination of chloroethenes. This framework differs from the existing state of the art by incorporating secondary isotopic effects and considering both C and Cl isotopes simultaneously. A comprehensive general model (GM), which is expected to be the closest representation of reality thus far investigated, was implemented. A less computationally intensive simplified model (SM), with the potential for use in modelling of complex reactive transport scenarios, was subsequently validated based on its comparison to GM. The approach of GM considers all isotopocules (i.e. molecules differing in number and position of heavy and light isotopes) of each chloroethene as individual species, of which each is degraded at a different rate. Both models GM and SM simulated plausible C/Cl isotopic compositions of tetrachloroethene (PCE), trichloroethene (TCE) and cis-1,2-dichloroethene (cDCE) during sequential dechlorination when using experimentally relevant kinetic and isotopic parameters. The only major difference occurred in the case where different secondary isotopic effects occur at the different non-reacting positions when PCE is dechlorinated down to cDCE. This observed discrepancy stems from the unequal Cl isotope distribution in TCE that arises due to the occurrence of differential secondary Cl isotopic effects during transformation of PCE to TCE. Additionally, these models are shown to accurately reproduce experimental data obtained during reductive dechlorination by bacterial enrichments harbouring Sulfurospirillum spp. where secondary isotope effects are known to have occurred. These findings underscore a promising future for the development of reactive transport models that incorporate isotopic parameters.

Contactless Respiration Monitoring in Real-Time via a Video Camera

Until today, vital signs monitoring in neonatal intensive care units (NICUs) is based on wired sensors, known to cause discomfort and false alarms. In view of overcoming such issues we investigate a contactless method for respiration monitoring by means of a simple video camera. Unlike many other solutions proposed in the literature, our approach makes use of a motion estimation with low computational complexity which facilitates a real-time implementation. To do so, the input image is split into blocks, for each of which motion is estimated. Thereafter, these block motions are classified according to their likelihood to contain true respiratory activity, enabling an automatic region of interest detection. Aside from the respiratory rate (RR) our algorithm also computes a quality index, representing the confidence of the given RR. The proposed approach was tested and evaluated on 16 healthy adults, both during illuminated and dark conditions, using a color or near-infrared camera, respectively. On more than 2 hours of recording, Bland-Altman analysis reveals an error of 0.2 ± 2.3 bpm (breaths-per-minute) when compared to the reference measure, a thoracic strain gauge belt. Our analysis further indicates that – independent of light or dark conditions – the near-infrared camera alone is sufficient to achieve satisfying results. These findings pave the way towards a simple, low-cost and contactless RR monitoring. While currently only tested on healthy adults, future work includes the evaluation of this approach in clinical scenarios, such as NICUs in particular.

Performance of Systolic Blood Pressure estimation from radial Pulse Arrival Time (PAT) in anesthetized patients

The performance of estimating Systolic Blood Pressure (SBP) in anesthetized patients via Pulse Arrival Time (PAT) techniques was studied with respect to the minimum required time in between two recalibration procedures.