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445 results for “arrhythmia”
Dataset for "Analysis of cardiac arrhythmia sources using Feynman diagrams"
<p>This archive contains the numerical methods presented in the publication "Analysis of cardiac arrhythmia sources using Feynman diagrams" as well as the data sets these methods have been applied on. The Python module for Ithildin contains the actual Python source code of those methods. Additional Python scripts have been used to generate the figures in the paper (.py files). The optical voltage mapping data (optical_*) has been slightly pre-processed (noise reduction, re-scaling, etc). The other files contain simulation results from several finite differences simulations of the mono-domain model.</p> <p>Please cite this paper when using the implementation: Arno L, Kabus D, Dierckx H (2023) Analysis of cardiac arrhythmia sources using Feynman diagrams.<a href="https://doi.org/10.48550/arXiv.2307.01508">https://doi.org/10.48550/arXiv.2307.01508</a></p>
Reducing False Arrhythmia Alarms in the ICU - The PhysioNet Computing in Cardiology Challenge 2015
<p>This dataset is part of the available dataset for <em>The PhysioNet Computing in Cardiology Challenge 2015</em>, available at https://www.physionet.org/content/challenge-2015/1.0.0/training.zip (last accessed today 2021-03-24).</p> <p>The dataset is licensed under GNU GPL license Version 3:</p> <p>Permissions:</p> <ul> <li>Commercial use</li> <li>Distribution</li> <li>Modification</li> <li>Patent use</li> <li>Private use</li> </ul> <p>Conditions:</p> <ul> <li>Disclose source</li> <li>License and copyright notice</li> <li>Same license</li> <li>State changes</li> </ul> <p>Limitations:</p> <ul> <li>Liability</li> <li>Warranty</li> </ul> <p>For more information about the license, check: https://choosealicense.com/licenses/gpl-3.0/</p> <p>The following modifications were made:</p> <ul> <li>Only the targets folder is used</li> <li>Only the files *.hea and *.mat are used, being the latter converted to CSV and compressed in .bz2 format</li> <li>Added the LICENSE.txt file as required.</li> </ul>
The Right Atrium Affects In Silico Arrhythmia Vulnerability in Both Atria
<h1>The Right Atrium Affects In Silico Arrhythmia Vulnerability in Both Atria</h1> <div> </div> <div><strong>Authors:</strong> Patricia Martínez Díaz, Jorge Sánchez, Nikola Fitzen, Ursula Ravens, Olaf Dössel, Axel Loewe</div> <div>patricia.martinez@kit.edu / publications@ibt.kit.edu</div> <div><a href="https://doi.org/10.1016/j.hrthm.2024.01.047">doi:10.1016/j.hrthm.2024.01.047</a></div> <div> </div> <div>This dataset contains 8 biatrial meshes and 8 monoatrial (left-only) meshes, derived from MRI and CT segmentations with annotations and fibers, ready for simulations in the cardiac electrophysiology simulator <a href="https://doi.org/10.1016/j.cmpb.2021.106223">openCARP</a>. We also provide the code to reproduce a total of 576 reentries by reading the selected parameters.par and state.roe files. The meshes were generated using <a href="https://github.com/KIT-IBT/AugmentA">AugmentA code</a> and the simulated reentries were induced following the <a href="https://doi.org/10.3389/fphys.2021.656411">PEERP protocol</a> by Azzolin et al. A carputils bundle containing the <a href="https://doi.org/10.35097/1830">openCARP experiment</a>, along with all associated parameters, is publicly available. The original cardiac segmentations are part of Krueger M. et al. Personalization of atrial anatomy and electrophysiology as a basis for clinical modeling of radio-frequency ablation of atrial fibrillation <a href="https://doi.org/10.1109/tmi.2012.2201948">doi:10.1109/TMI.2012.2201948</a></div> <div> </div> <h2>Folder structure</h2> <div>The code is located in the `src` folder, the meshes in the `data` folder and the reentries in the `results` folder. </div> <div>```</div> <div>KIT_2/</div> <div>|-- src/</div> <div> |-- run.py</div> <div> |-- induceReentry.py</div> <div> |-- getStimPoints.py</div> <div> |-- element_tag.csv</div> <div> |-- al_mk_H.par</div> <div> |-- requirements.txt</div> <div> |-- reproduceReentry.py</div> <div>|-- data/</div> <div> |-- meshes/</div> <div> |-- P1/ </div> <div> |-- monoatrial/ </div> <div> |-- biatrial/</div> <div>.</div> <div>.</div> <div>.</div> <div> |-- P8 </div> <div> |-- monoatrial/ </div> <div> |-- biatrial/</div> <div>|-- results/</div> <div>|-- MESH_SCENARIO_STATE_CHAMBER/ (e.g P1_bi_M_LA) </div> <div>|-- point_X_beat_Y</div> <div>| </div> <div>|-- README.md</div> <div>```</div> <div> </div> <div>`src`: contains the source files needed to run PEERP protocol </div> <div> <ul> <li>`run.py` This is the main function to run the pacing protocol (not needed to run if reentries are only reproduced, check reproduceReentry.py)</li> <li>`induceReentry.py` Contains a list of pacing protocols. The PEERP protocol is included here</li> <li>`getStimPoints.py` Extract the stimulation points</li> <li>`element_tag.csv` Region tag numbering</li> <li>`al_mk_H.par` Par file with ionic scaling factors for three states; H:Healthy, M:Mild, S:Severe</li> <li>`requirements.txt` Packages to create the virtual enviroment. (This was my output of ```pip3 list> requirements.txt```)</li> <li>`reproduceReentry.py` Reentries can be reproduced given a selected folder where the .par and .roe files are stored.</li> </ul> </div> <div> </div> <div>`data`: contains the `meshes` folder with the bilayer meshes in openCARP (.elem, .lon and .pts) and .vtk format. Synthetic fibrotic distributions are included in the the .regele files.</div> <div> <ul> <li>`meshes/P1/monoatrial/LA_stim_points.txt` stimulation points for the PEERP protocol</li> <li>`meshes/P1/monoatrial/LA_bilayer_with_fiber_elems_not_conductive_M.regele` Element ids corresponding to synthetic fibrotic distribution with respect to remodeling states M and S. H state was modelled without fibrosis</li> </ul> </div> <div> </div> <h2>Reproduce the reentries </h2> <div>You can generate the .igb file of a specific reentry by selecting the corresponding folder in the results directory. An example is given to reproduce the reentry in P1_bi_M_LA/point_0_beat_2/reproduce_reentry.igb. Select the folder `--par_file_directory`and set `--tend` to define the duration of the simulation in miliseconds.</div> <div>_HINT: We recommend keeping the folder structure so that the other parameters, such as: mesh, scenario, state and chamber, can be read from the --par_file_directory. Otherwise, the meshes and results directories need to be modified._</div> <div>```</div> <div>cd src/</div> <div>reproduceReentry.py --par_file_directory ../results/P1_bi_M_LA/point_0_beat_2 --tend 1500</div> <div> </div> <div>```</div> <div></div> <div> </div> <div> </div> <h2>Preparation before running the PEERP pacing protocol</h2> <div> </div> <div>Follow the next steps if you want to run the PEERP pacing protocol, either for the provided meshes or for your own meshes. To run the PEERP protocol in a controlled environment, it is recommended, before running the run.py, to create a virtual environment. Go to your terminal and type: </div> <div>```</div> <div>cd src/</div> <div>python3 -m venv ./myEnv</div> <div>source ./myEnv/bin/activate</div> <div>pip3 install -r requirements.txt</div> <div>```</div> <div> </div> <div>You need to add carputils to your `PATH`. You can run the code in the terminal or use and IDE to debug the code. </div> <div>Note: I am using PyCharm 2020.3. and in Settings --> Python interpreter --> show all and then in the (+) symbol, add the path to carputils there:</div> <div> </div> <div>Otherwise you can add this extra lines at the beginning of `run.py``:</div> <div>```</div> <p># Replace '/path/to/carputils' with the actual path to your carputils package</p> <p>carputils_path = '/path/to/carputils'</p> <p># Add the carputils path to sys.path</p> <div>sys.path.append(carputils_path)</div> <div>```</div> <h2>Run the PEERP protocol</h2> <div> </div> <div>The following example runs the PEERP from a single stimulation point. If you want to run PEERP over all the points, simply add the flag --run_all_points 1 </div> <div>```</div> <div>cd src/</div> <div>python3 run.py --giL 0.4166 --geL 1.458 --cv 0.8 --mesh monoatrial --protocol PEERP --pacing 122718 --stim_file LA_stim_points.txt --geometry LA_bilayer_with_fiber_um --cell_bcl 500 --model Courtemanche --ionic_prop_file al_mk_S.par --max_n_beats_PEERP 1 --overwrite-behaviour overwrite</div> <div>```</div> <div> </div> <h2>Running your own experiment and making your own changes</h2> <div>Extract the stimulation points on your mesh, where the PEERP protocol will be run: </div> <div>```</div> <div>python3 getStimPoints.py --mesh monoatrial --tolerance 20000 --stim_file LA_stim_points.txt --chamber LA</div> <div>```</div> <div> </div> <div>Tune conduction velocity (CV) and conductivites. The code expects the intracellular end extracellular longitudinal conductivity values as an input. We used `tuneCV` to fit CV=0.7m/s with dx=0.4mm and dt=20us</div> <div>If you want to adjust the values, run in the terminal:</div> <div>```</div> <div>tuneCV --resolution 400 --model Courtemanche --velocity 0.7 --converge True --sourceModel monodomain --surf True --dt 20</div> <div>```</div> <div>You can provide the location of the start of the activation by selecting the desired point ID:</div> <div> <ul> <li>Load the mesh in Paraview (or Meshalyzer)</li> <li>click on the ? symbol</li> <li>save the ID and change the `--pacing` argument </li> </ul> </div> <div> </div> <div>Call `run.py` with a new mesh. The protocol starts by prepacing the mesh and then using the last beat as initial condition tu run the PEERP.</div> <div>Be aware that for a monoatrial mesh you might need to give the new id for the location of the earliest activation. Change `12345` to your desired point ID.</div> <div>```</div> <div>python3 run.py --mesh newMesh --pacing 12345 --protocol prepace --stim_file LA_stim_points.txt</div> <div>```</div> <div> </div> <div>Run the protocol with different electrical remodelling stage. You can change the .par file or select one file from the three provided: </div> <div>```</div> <div>python3 run.py --mesh newMesh --pacing 12345 --protocol PEERP --stim_file LA_stim_points.txt --args.ionic_prop 'l_mk_M.par'</div> <div>```</div> <div> </div> <div>You can also try to run a biatrial example. The biatrial mesh is also provided. You need to extract the points on the RA surface using `getStimPoints.py`, to run the RA experiment: </div> <div>```</div> <div>cd src</div> <div>python3 getStimPoints.py --mesh biatrial --tolerance 20000 --stim_file RA_stim_points.txt --chamber RA</div> <div>```</div> <div>Then run PEERP twice, one per each chamber:</div> <div> </div> <div>```</div> <div>python3 run.py --mesh biatrial --geometry LA_RA_bilayer_with_fiber --pacing 12345 --stim_file LA_endo_2cm.txt --args.ionic_prop 'l_mk_M.par'</div> <div>python3 run.py --mesh biatrial --geometry LA_RA_bilayer_with_fiber --stim_file LA_stim_points.txt --args.ionic_prop 'l_mk_M.par'</div> <p> </p>
Patient-specific induced pluripotent stem cell properties implicate Ca2+-homeostasis in clinical arrhythmia associated with combined heterozygous RYR2 and SCN10A variants
<p class="MsoNormal"><span>We illustrate the use of induced pluripotent stem cells (iPSCs) as platforms for investigating cardiomyocyte phenotypes in a human family pedigree exemplified by novel heterozygous RYR2-A1855D and SCN10A-Q1362H variants occurring alone and in combination. The proband, a four-month-old boy, presented with </span><span>polymorphic</span><span> ventricular tachycardia (</span><span>P</span><span>VT). Genetic tests revealed double novel heterozygous RYR2-A1855D and SCN10A-Q1362H variants inherited from his father (F) and mother (M) respectively. His father showed ventricular premature beats (VPB); his mother was asymptomatic. Molecular biological characterisations demonstrated greater <em>TNNT2</em> mRNA expression in the iPSCs-induced cardiomyocytes (iPS-CMs) than in the iPSCs</span><span>.</span><span> </span><span>c</span><span>TNTs became progressively organised, but cytoplasmic RYR2 and SCN10A aggregations occurred in the iPS-CMs. Proband-specific iPS-CMs showed decreased <em>RYR2</em> and <em>SCN10A</em> mRNA expression. The RYR2-A1855D variant resulted in premature spontaneous sarcoplasmic reticular (SR) Ca<sup>2+</sup> transients (PCTs), Ca<sup>2+</sup> oscillations (COs), and increased action potential durations (APDs). SCN10A-Q1362H did not confer any specific phenotype. However, the </span><span>combined </span><span>heterozygous RYR2-A1855D and SCN10A-Q1362H variants in the proband iPS-CMs resulted in accentuated Ca<sup>2+</sup> homeostasis disorders, AP prolongation and susceptibility to early afterdepolarisations (EADs) at high stimulus frequencies. These findings attribute the clinical phenotype in the proband to effects of the heterozygous <em>RYR2</em> variant exacerbated by heterozygous <em>SCN10A</em> modification. </span></p>
Dataset to reproduce firgures for the paper "A unifying method to study Respiratory Sinus Arrhythmia dynamics implemented in a new toolbox"
<p>Dataset provided to reproduce figures <br> for the paper "A unifying method to study Respiratory Sinus Arrhythmia dynamics implemented in a new toolbox"</p> <p>Jupyter notebooks are available here:<br> https://github.com/samuelgarcia/physio_benchmark</p> <p>Human dataset<br> =============</p> <p>Context: A research aimed to decipher the impact of respiration on brain oscillations</p> <p>Data collection methods: ECG and Respiration of 15 healthy adults subjects <br> (age : 30.9 +/- 9.5 yo).All participants gave informed consent to take part to the study, and all experiments<br> were approved by the national french committee (CPP number 4090). They were sitting quietly and instructed just<br> to relax. Recording lasted 5 minutes. Respiration signal was recorded from a nasal sensor<br> (Sensortechnics GmbH, Puchheim , Germany) at a sampling rate of 1000 Hz, amplified by actiCHamp<br> Plus amplifier (Brain Products GmbH, Gilching, Germany). ECG signal was recorded from 3 skin electrodes<br> (right forearm, left forearm, left iliac region), at a sampling rate of 1000 Hz (same amplifier).</p> <p>Structure of files: tabular separated values text files.<br> The first columns correspond to the ECG signal, the second is the respiratorysignal.<br> The sampling rate is 1000Hz</p> <p>Data manipulations: The original dataset has longer durationand and contain channels (EEG).<br> This sub-dataset was extracted from the original using the neo python package from the VHDR brain product format.<br> Signal tarces haven't been preprocessed they correspond to the "raw" signal.</p> <p><br> Data confidentiality and permissions: Experiments were approved by the national french committee (CPP number 4090)</p> <p><br> Animal dataset<br> ==============</p> <p>Context: The dataset was recorded to validate the device telemetric jacket from Etisense.</p> <p>Data collection methods: ECG and Respiration of 1 adult rat were recorded. Recording lasted 30 seconds during<br> freely behaving. Respiration signal and ECG were recorded from a thoraco-abdominal telemetric<br> jacket at which it was habituated before. Recorded were done at a sampling rate of 500 Hz, amplified by<br> Etisense acquisition unit (Etisense, MedTech company, Lyon, France).</p> <p>Structure of files: tabular separated values text files.<br> The first columns correspond to the ECG signal, the second is the respirator signal.<br> The sampling rate is 500Hz</p> <p><br> Data manipulations:<br> The dataset was extracted from the original HDF5 structure.<br> The ECG signal correcpond to the "raw" traces from the HDF5 files.<br> The respiratory signal was originaly sample at 200Hz on the device and resample with linear interpolation<br> to 500Hz to be easy aligned with the ECG signal.</p> <p>Data confidentiality and permissions: Experiments were carried according to the ethical guidelines of the<br> European Communities Council Directive of 24 November 1986 (86/609/EEC), as well as the approval 16979 of the<br> Lyon 1 University CEEA-55 ethical committee and of the Ministry of Higher Education, Research and Innovation.<br> </p>
Patient-specific induced pluripotent stem cell properties implicate Ca2+-homeostasis in clinical arrhythmia associated with combined heterozygous RYR2 and SCN10A variants
Open the record for dataset details and reuse information.
Predicting arrhythmia recurrence post-ablation in atrial fibrillation using explainable machine learning: Code repository
Open the record for dataset details and reuse information.
Predicting arrhythmia recurrence post-ablation in atrial fibrillation using explainable machine learning: Atrial meshes
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Population-based computational simulations elucidate mechanisms of focal arrhythmia following stem cell injection
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Surrogate models to assess the induction of arrhythmias during virtual programmed ventricular stimulation
<p>These videos show a simulation of the Programmed Ventricular Stimulation (PVS). The first video represents the entire mesh simulation, and the second video the surrogate model.</p>
Graft‐host coupling changes can lead to engraftment arrhythmia: A computational study
<p>This dataset contains examples and raw data related to the cited publication (doi: 10.1113/jp284244). Computational models derived from histological images are provided. Raw values in the data spreadsheet are given as fraction of simulations for a particular configureation that resulted in graft-initiated host excitation.</p>
A Study Assessing Arrhythmia Mapping With the Multi-Electrode OPTRELL™ Mapping Catheter
ClinicalTrials.gov study NCT04983797. IPD Sharing: YES. Countries: 2. Publications: 14.
CARISMA : Cardiac Arrhythmias and Risk Stratification After MyoCardial Infarction
ClinicalTrials.gov study NCT00145119. IPD Sharing: Not stated. Countries: 1. Publications: 9.
A Study Assessing Arrhythmia Mapping With a Globe-Shaped, High-Density, Multi-Electrode Mapping Catheter
ClinicalTrials.gov study NCT05373862. IPD Sharing: YES. Countries: 3. Publications: 15.
Assessing Arrhythmias After Ablation Using Implantable Recorders
ClinicalTrials.gov study NCT01176617. IPD Sharing: Not stated. Countries: 1. Publications: 1.
A Clinical Trial Utilizing Dantrolene in Patients With Ventricular Arrhythmias.
ClinicalTrials.gov study NCT04134845. IPD Sharing: NO. Countries: 1. Publications: 1.
Continuous Cardiac Arrhythmia Monitoring in Hemodialysis Patients
ClinicalTrials.gov study NCT00932659. IPD Sharing: Not stated. Countries: 1. Publications: 6.
Intramural Needle Ablation for the Treatment of Refractory Ventricular Arrhythmias
ClinicalTrials.gov study NCT03204981. IPD Sharing: NO. Countries: 1. Publications: 2.
Avoid DeliVering TherApies for Non-sustained Arrhythmias in ICD PatiEnts III
ClinicalTrials.gov study NCT00617175. IPD Sharing: Not stated. Countries: 14. Publications: 7.
Cardiac Arrhythmias in Patients Undergoing Kidney Cancer Surgery Depending on the Anaesthesia Method
ClinicalTrials.gov study NCT02988219. IPD Sharing: NO. Countries: 1. Publications: 1.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.