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167 results for “electrical impedance”
Recruitment Maneuvers and PEEP-guided Electrical Impedance Tomography for Abdominal Laparoscopic Surgery Patients
ClinicalTrials.gov study NCT06850844. IPD Sharing: NO. Countries: 1. Publications: 4.
Comparison of Different Methods to Calculate Pendelluft by Electrical Impedance Tomography in Mechanically Ventilated Patients
ClinicalTrials.gov study NCT06494215. IPD Sharing: NO. Countries: 1. Publications: 16.
Positive Expiratory Pressure (PEP) and Continuous High-frequency Oscillation (CHFO) on Lung RECRUITment Using Electrical Impedance Tomography (EIT) - The PEP-RECRUIT Study
ClinicalTrials.gov study NCT06689020. IPD Sharing: NO. Countries: 1. Publications: 5.
Using Electrical Impedance Tomography to Investigate the Relationship Between Airflow Rate During High-flow Oxygen Therapy and Pressure in Patients With Heart Failure Compared to Non-invasive Ventilat
ClinicalTrials.gov study NCT06107907. IPD Sharing: UNDECIDED. Countries: 1. Publications: 8.
Application of Indicator Based-electrical Impedance Tomography Method Assess Lung Regional Perfusion in ICU Patients
ClinicalTrials.gov study NCT04081142. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.
Electrical Impedance Myography: Natural History Studies inNeuromuscular Disorders and Healthy Volunteers
ClinicalTrials.gov study NCT01900132. IPD Sharing: UNDECIDED. Countries: 1. Publications: 3.
Comparison of Computertomography Scan, Electrical Impedance Tomography, and Ultrasound of the Lung in Infants
ClinicalTrials.gov study NCT04989439. IPD Sharing: NO. Countries: 1. Publications: 15.
Use of Electrical Impedance Spectroscopy (EIS) for Early Diagnosis of Skin Damage
ClinicalTrials.gov study NCT04688749. IPD Sharing: Not stated. Countries: 1. Publications: 3.
An open-source and easily replicable hardware for Electrical Impedance Tomography
<p>Supplementary materials of the paper.</p>
Stimulus classification with electrical potential and impedance of living plants: comparing discriminant analysis and deep-learning methods
<p>The physiology of living organisms, such as living plants, is complex and particularly difficult to understand on a macroscopic, organism-holistic level. Among the many options for studying plant physiology, electrical potential and tissue impedance are arguably simple measurement techniques that can be used to gather plant-level information. Despite the many possible uses, our research is exclusively driven by the idea of phytosensing, that is, interpreting living plants’ signals to gather information about surrounding environmental conditions. As ready-to-use plant-level physiological models are not available, we consider the plant as a blackbox and apply statistics and machine learning to automatically interpret measured signals. In simple plant experiments, we expose <em>Zamioculcas zamiifolia</em> and <em>Solanum lycopersicum</em> (tomato) to four different stimuli: wind, heat, red light and blue light. We measure electrical potential and tissue impedance signals. Given these signals, we evaluate a large variety of methods from statistical discriminant analysis and from deep learning, for the classification problem of determining the stimulus to which the plant was exposed. We identify a set of methods that successfully classify stimuli with good accuracy, without a clear winner. The statistical approach is competitive, partially depending on data availability for the machine learning approach. Our extensive results show the feasibility of the blackbox approach and can be used in future research to select appropriate classifier techniques for a given use case. In our own future research, we will exploit these methods to derive a phytosensing approach to monitoring air pollution in urban areas.</p> <p>Data repository for our paper " <em>Stimulus classification with electrical potential and impedance of living plants: comparing discriminant analysis and deep-learning methods</em> ", submitted to the journal Bioinspiration & Biomimetics . Please refer to the paper for more information.</p> <p> </p> <p><strong>Contents of this repository</strong></p> <ul> <li><em>mu_interface:</em> Code for our data collection plant experiments, based on Raspberry Pis and the <a href="http://cybertronica.co/?q=products/phytosensor">Cybertronica phytosensing and phytoactuating system</a>.</li> <li><em>SupplementaryCode</em>: Includes the discriminant analysis classifier, raw datasets, calculated features, test-train split and the corresponding code.</li> <li><em>dl-4-tsc:</em> Deep learning framework developed by <a href="https://doi.org/10.1007/s10618-019-00619-1">Fawaz et. al (Deep learning for time series classification: a review)</a> and adapted to our use case. </li> <li><em>DeepClassifier: </em>Trained deep learning time series classifier.</li> <li><em>classification_results.xlsx: </em>Overview of the results from the deep learning framework (accuracy, precision, recall, training time, confusion matrix) and the achieved accuracies using discriminant analysis with sequential forward section (further evaluation metrics of the discriminant analysis can be found in SupplementaryCode.</li> </ul>
Label-Free Differentiation of Living versus Dead Single Yeast Cells using Broadband Electrical Impedance Spectroscopy
<p>Data to support manuscript.</p>
Quantitative phenotyping of plant roots using spectral electrical impedance tomography: a rhizotron study on maize and bean crops with optimized measurement design
<div> <h1>Data repository</h1> <br> <div>In this repository, the data accompanying the publication "Quantitative phenotyping of plant roots using spectral electrical impedance tomography: a rhizotron study on maize and bean crops with optimized measurement design", published in "Plant Methods", is stored.</div> <div> </div> <div>The publication can be found at https://doi.org/10.1186/s13007-024-01247-7</div> <br> <h2>Dataset structure</h2> <br> <div>The dataset is structured in the following directories:</div> <br> <h3>complex resistivity inversions</h3> <br> <div>The folder "new_filter_approach_v4_allfreqs", contains all inversion results for frequencies from 0.79 Hz to 1 kHz, sorted by plant name. The data are given as tomodirs (see https://github.com/geophysics-ubonn/crtomo_tools for importing tomodir data within a python framework).</div> <br> <div>In the folder "scatterdata_from_invs", electrical parameters from the inversions are given for x-y-locations in the grid.</div> <br> <h3>dd_fit_results_all_cells</h3> <br> <div>In this folder, the results of the Debye-Decomposition for all cells in the grid are given, together with the x-y-locations in the grid.</div> <br> <h3>dd_fit_results_only_plants</h3> <br> <div>In this folder, the results of the Debye-Decomposition for all cells within the root zone in the grid are given, together with the x-y-locations in the grid.</div> <br> <h3>grid</h3> <br> <div>Contains all files to construct the inversion grid using crtomo_tools.</div> <div> </div> <br> <h3>images_plants</h3> </div> <div> <div> </div> <div>Contains photos for all measured plants, labeled according to the datasets in the manuscript.</div> <br> <div> <h3>impedance_data</h3> </div> <br> <div>In the folder "uncorrected", the raw impedance measurements for all plants are saved (only the correction for the resistivity magnitude is applied to account for the mismatch of 2D inversion and 3D measurement). The folder "corrected" containst the imepdance data that was corrected with background water measurements. The file "md_data_impedance.csv" contains the contact resistances and leakage currents for all measurements.</div> <br> <h3>optimization</h3> <br> <div>This folder contains the results for the synthetic modelling using an optimized (OPT_full) and reduced (OPT_middle) scheme, as described in the manuscript. As for the experimental results, inversions are given in tomodir format. The file "exp.csv" contains the xy-coordinates of the outline of the synthetic polarization signature.</div> <br> <h3>plots</h3> <br> <div>Contains all plots that are shown in the manuscript.</div> <br> <h3>root_data</h3> <br> <div>In the folder "root_area_polygons", the xy-coordinates of the outline of each root system in the rhizotron experiments, extracted from images of the front of the rhizotron, are saved. The file "extracted_elec_params_from_area_v4.csv" contains electical parameters from the inversion results for a number of frequencies for all cells that lie within the root area polygons. The file "Root_traits_final.csv" contains the individually measured root traits via weighing and scanning.</div> <br> <h3>scripts</h3> <br> <div>The scripts "0_plot_methods.py", "1_plot_results.py", "2_plot_discussion.py" and "3_plot_supplementary.py" create all plots shown in the manuscript and save them in the "plots"-directory.</div> </div> <p> </p> <p> </p>
Simulation Circuits for A Time-Domain Approach to Electrical Impedance Tomography using Numerical Analysis of the Step Response
<p>SImulation circuits for the paper of the same name presented in the conference.</p>
Electrical Impedance Tomography (EIT) Monitoring in Adults With ALI or ARDS
ClinicalTrials.gov study NCT01272882. IPD Sharing: NO. Countries: 1. Publications: 0.
Bedside PEEP Selection in by Electrical Impedence Tomography During Mechanical Ventilation in Children
ClinicalTrials.gov study NCT07319624. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
DART Electrical Impedance Myography (EIM) Trial in Duchenne Muscular Dystrophy (DMD) and Healthy Controls
ClinicalTrials.gov study NCT01523964. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Correlation of Survival in Puerperae by Electrical Impedance Tomography
ClinicalTrials.gov study NCT03715647. IPD Sharing: NO. Countries: 0. Publications: 13.
Monitoring of Lung Ventilation Through Electrical Impedance Tomography During Pediatric Surgery
ClinicalTrials.gov study NCT06370507. IPD Sharing: Not stated. Countries: 0. Publications: 1.
Electrical Impedance Tomography in Guiding Early and Precise Rehabilitation of Patients with Difficult Ventilator Weaning
ClinicalTrials.gov study NCT06677099. IPD Sharing: NO. Countries: 0. Publications: 1.
Clinical Utility of Electrical Impedance Spectroscopy
ClinicalTrials.gov study NCT04161742. IPD Sharing: NO. Countries: 0. Publications: 2.
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