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10,068 results for “Heart”
In-vitro dataset for classification and regression of stenosis: dependence on heart rate, waveform and location
<p><strong>Background</strong></p> <p>This data supplements the paper "Classification and regression of stenosis using an in-vitro pulse wave dataset:<br> dependence on heart rate, waveform and location". It was created at Technische Hochschule Mittelhessen (THM) in Germany and uploaded to Zenodo. Please cite the paper (<a href="https://doi.org/10.1016/j.compbiomed.2022.106224">https://doi.org/10.1016/j.compbiomed.2022.106224</a>) and the Zenodo doi when using this dataset.</p> <p><strong>General description / Dataset structure</strong></p> <p>Each mat-File describes a different measurement (details can be found in the paper). There are 17 pressure signals for different positions, one flow sensor close to the stenosis location and one monitor signal of the proportional valve use to control the input curve. Total duration of each signal is 60s with a sampling rate of 1000 Hz. Each mat-file contains a header structure with metadata and struct array for signals of each sensor. Signals in each mat-File are aligned with respect to a common time axis, but this is not guaranteed between different measurements/files. We did our best to make the beginnings end endings align as close as possible (by removing buffer artefacts and aligning the input signal of the monitor), however algorithms should not rely on a global time axis. This similar to patient measurements without an ekg, this does also not share a global time axis comparable among patients.</p> <p>The file format can either be loaded directly in Matlab or in Python with scipy's loadmat function.</p> <p>The data is structure first by stenosis "state" (or location) then by heart rate and then by heart waveform. The stenosis "states" can devided in 1 subset of 10 folders created for regression and 6 created for classification. Excerpt of the folder structure:</p> <ul> <li>No Stenosis <ul> <li>HR 50 <ul> <li>WaveForm1.mat</li> <li>WaveForm2.mat</li> <li>...</li> </ul> </li> <li>HR 55 <ul> <li>...</li> </ul> </li> <li>...</li> </ul> </li> <li>Regression - Stenosis at Pos01 <ul> <li>HR 50 <ul> <li>...</li> </ul> </li> <li>...</li> </ul> </li> <li>...</li> </ul> <p>The tools also available at this page help with traversing this folder structure and are available for Python and Matlab.</p> <p><strong>Data Fields of each file</strong></p> <table> <caption>headerStruct</caption> <thead> <tr> <th scope="col">field</th> <th scope="col">description</th> </tr> </thead> <tbody> <tr> <td>id</td> <td>internal database id</td> </tr> <tr> <td>name</td> <td>stenosis location</td> </tr> <tr> <td>rate</td> <td>sampling rate in Hz</td> </tr> <tr> <td>description</td> <td>definition of automatic parameter sweep range</td> </tr> <tr> <td>configuration</td> <td>concrete parameters of the trapezoidal input curve (offset and amplitude in mmHg, ascend times and descend times and smoothing window in a fraction the time period (1.2s))</td> </tr> </tbody> </table> <p> </p> <table> <caption>signalStruct</caption> <thead> <tr> <th scope="col">field</th> <th scope="col">description</th> </tr> </thead> <tbody> <tr> <td>nodeId</td> <td>corresponds to numbered nodes at which the sensor is placed, the corresponding location can be found in the technical paper describing the MACSim simulator (node numbering, not sensor numbers) or in the software SISCA in the example database.</td> </tr> <tr> <td>type</td> <td>'p' ... pressure or 'q' ... flow</td> </tr> <tr> <td>data</td> <td>double array, time series of each sensor, unit mmHg for type 'p' and ml/s for type 'q'</td> </tr> <tr> <td>anatomicalPosition</td> <td>name of the corresponding anatomical position</td> </tr> </tbody> </table> <p><strong>Tools:</strong></p> <p>This Tools should make it easier to load the dataset. The usage is documented in the respective code files.</p> <p>Code for the publication is available here:<br> https://gitlab.com/agbernhard.lse.thm/publication_macsim_machinelearning<br> </p> <p> </p> <p> </p>
Data of Mitochondrial respirometric data of adult males of Octopus maya: supporting the method to evaluate heart metabolic activity
<p>Mitochondrial respirometry is key to understanding how environmental factors model energetic cellular processes. Until now, no reports have shown temperature effects and other environmental variables on cephalopod mitochondria activity because of the lack of a method to evaluate mitochondrial respiratory parameters on those groups of species. In this sense and for the first time, it showed the mitochondrial respirometry data of adult Octopus maya’s heart. Following the protocol is illustrated a step-by-step procedure to get the corresponding respiratory parameters. </p>
Supplementary material to the article "Finding a way into an interpreter's heart: Methodological considerations on heart-rate variability building on an exploratory study"
<p>Supplementary material to the article "Finding a way into an interpreter's heart: Methodological considerations on heart-rate variability building on an exploratory study" by Nicoletta Spinolo, Christian Olalla-Soler & Ricardo Muñoz Martín. </p> <p>The ZIP file contains the four texts that were used in the study reported in the article. </p> <p>Reference:</p> <p>Spinolo, Nicoletta; Olalla-Soler, Christian, Muñoz Martín, Ricardo (202X). "Finding a way into an interpreter's heart: Methodological considerations on heart-rate variability building on an exploratory study". </p>
Genome-wide association and multi-trait analyses characterize the common genetic architecture of heart failure
<p>Genome-wide association study summary statistics.</p>
Half of a twin capital with a motif of hearts
Half of a twin capital with a motif of three hearts The presented capital is one of seven halves of twin capitals uncovered during the years 1947–1953 by archaeologists during research in the course of reconstructing eastern wing of the monastery. The head is considered as one of the best examples of Romanesque masonry in Poland. It comes from the first monastic complex in Tyniec. However, researchers do not agree whether the twin capitals discovered in Tyniec were originally located in the church or in the cloisters. The capital has the shape of a truncated pyramid turned upside down. The obverse is filled by ornamentation consisting of little stalks, but the pattern is barely discernible due to some defects and abrasion of the surface. On the reverse, there is decoration in the form of palmettes enclosed by a floral scroll, which forms the shape of three large hearts. 4th quarter of the 11th century, Tyniec Museum of the Benedictine Abbey in Tyniec Inventory number: DA/168/2012 Source: Objaverse 1.0 / Sketchfab
Lamp Hearts 01
The earliest heart-shaped charges in heraldry appear in the 12th century; the hearts in the coat of arms of Denmark go back to the royal banner of the kings of Denmark, in turn based on a seal used as early as the 1190s. However, while the charges are clearly heart-shaped, they did not depict hearts in origin, or symbolize any idea related to love. Instead, they are assumed to have depicted the leaves of the water-lily. Early heraldic heart-shaped charges depicting the leaves of water-lilies are found in various other designs related to territories close to rivers or a coastline (e.g. Flags of Frisia). Four hearts put together in the shape of a glover, cross or a star. It is a ceiling lighting fixture projecting light down from above. Usually produced in formed glass but could be done in plastic. Hexagon with a shape of a shield. A flower or a star. The North Star depicts a beacon of inspiration and hope to many. Not necessarily in the formation of the five corner perfect star but resembling more NATO flag. Source: Objaverse 1.0 / Sketchfab
Mount Yuraktau (heart-mountain)
Russia, Republic of Bashkortostan, Sterlitamak district. April 2, 2020. Single mountain. Natural monument. The rest of the reef, Lower Permian (Late Paleozoic) reef massif, formed over 230 million years ago in the tropical sea. The length is 1 km, the width is 850 m, the height above the Belaya River is 220 m, above the soil level is 200 m, the absolute height above sea level is 338 m. It has a cone-shaped shape. Slopes - 20-30 degrees, but do not form rocky ledges. The lower part is covered with scree. At the base of the northern slope of the mountain there are springs, one with sulphurous water. At the foot of Yuraktau is located Lake Moksha. Source: Objaverse 1.0 / Sketchfab
Heart of Brother Saint André...
Coeur du frère André, exposé dans une salle l'Oratoire St-Joseph de Montréal. Luminosité très faible, forte occlusion et réflection, ce qui rendit la capture de photos problématique... Source: Objaverse 1.0 / Sketchfab
Main Door-Basilica of the Heart of Jesus Zagreb
Another piece from Zagreb, this is the main door of a jesuit church there called Basilica of the Heart of Jesus. Source: Objaverse 1.0 / Sketchfab
Snuffbox in the shape of a heart
ID no.: FZRS/94 Museum: Collection of the Sosenko Family Foundation https://muzea.malopolska.pl/en/objects-list/1614 Digitalisation: RDW MIC, Virtual Małopolska project Source: Objaverse 1.0 / Sketchfab
Validation of transpulmonary thermodilution variables in hemodynamically stable patients with heart diseases - Individual patient data
<p>This dataset contains individual subject data for hemodynamic measurements assessed in the present study.</p>
HEartS Professional Survey: Charting the effects of COVID-19 on working patterns, income, and well-being among arts professionals in China (October 2020, August 2021)
<p>These data were collected using the HEartS Professional China survey from performing arts workers in China in October 2020 and August 2021. HEartS Professional China is an adaptation of the HEartS Professional surveys which were used in 2020-2021. All the surveys were designed as multi-strategy data collection tools with two main purposes: (1) to chart working patterns, income, sources of support, and indicators of mental and social well-being to identify trends in the effects of the lockdown at the time and (2) to explore the individual work and wellbeing experiences of performing arts professionals in their own words, to identify the subjective effects of lockdown in terms of challenges and opportunities. The survey covers six areas: 1) demographics; (2) information on illness or self-isolation related to COVID-19; (3) work profiles and income; (4) changes to work profiles and income as a result of the pandemic, as well as sources of support; (5) open-response questions about work and wellbeing experiences of lockdown including challenges and opportunities; and (6) validated measures of health, wellbeing, and social connectedness. The HEartS Professional surveys are adaptations of the HEartS Survey which charts the Health, Economic, and Social impacts of the ARTs (<a href="https://doi.org/10.5061/dryad.3r2280gdj">https://doi.org/10.5061/dryad.3r2280gdj</a>).</p>
Map 1 in Fisheries at the heart of a development issue in Mauritania: Small coastal pelagics between market logic and nutritional rationality
Map 1: Geographical areas of small pelagics fishing and landings
Fig 3 in Fisheries at the heart of a development issue in Mauritania: Small coastal pelagics between market logic and nutritional rationality
Fig 3: Contribution of small coastal pelagics to public finances
Fig 2 in Fisheries at the heart of a development issue in Mauritania: Small coastal pelagics between market logic and nutritional rationality
Fig 2: Evolution of the quantities of fish distributed by the SNDP
Fig 1 in Fisheries at the heart of a development issue in Mauritania: Small coastal pelagics between market logic and nutritional rationality
Fig 1: Evolution of fishmeal and fish oil production in Mauritania
A synthetic dataset for the exploration of survival and classification models: prediction of heart attack or stroke within a 10-year follow-up period
<div> <div></div> </div> <div> <div> <div> <p><span>Machine learning methodologies are increasingly popular in health care research. This shift to integrated data science approaches necessitates professional development of the existing health care data analyst workforce. To enhance a smooth transition, educational resources need to be developed. Barriers to accessing real healthcare datasets, vital for health care data analyses methodologies training purposes, include financial, ethical and patient confidentiality concerns. Synthetic datasets mimicking real-world complexities offer a simpler solution.</span></p> <p>We present a synthetic dataset which mirrors routinely collected primary care data on heart attack and stroke among the adult population. The data incorporates much of the practical challenges encountered in routinely collected primary care systems such as missing data, informative censoring, interactions, variable irrelevance, and noise and can be used for training in methods which handle these difficulties. The intent is for the user to build models of heart/stroke risk using survival-based methodologies.</p> <p>By sharing this synthetic dataset openly, our goal is to contribute a transformative asset for professional training in health and social care data analysis. The dataset covers demographics, lifestyle variables, comorbidities, systolic blood pressure, hypertension treatment, family history of cardiovascular diseases, respiratory functioning, and experience of heart-attack and/or stroke. This initiative aims to bridge the gap in sophisticated healthcare datasets for training, fostering professional development of the health and social care research workforce.</p> <p>This study is funded by the National Institute for Health and Care Research ARC Wessex and the National Centre for Research Methods. The views expressed in this summary are those of the author(s) and not necessarily those of the National Institute for Health and Care Research or the Department of Health and Social Care.</p> <p> </p> </div> </div> </div>
Data from: Non-inheritable risk factors during pregnancy for congenital heart defects in offspring: a matched case-control study
<p>Data analyzed in "Non-inheritable risk factors during pregnancy for congenital heart defects in offspring: a matched case-control study". The data provided by the authors to benefit other researchers. The posted materials are not copyedited and are the sole responsibility of the authors, so questions should be addressed to the corresponding author.</p>
Characterization of NAD(P)H and FAD autofluorescence signatures in an isolated-perfused rat heart model
<p>Raw data concerning publication titled "Characterization of NAD(P)H and FAD autofluorescence signatures in an isolated-perfused rat heart model"</p> <p>Abstract</p> <p>Autofluorescence spectroscopy is a promising label-free approach to characterize biological samples with demonstrated potential to report structural and biochemical alterations in tissues in a number of clinical applications. We report a characterization of the ex vivo autofluorescence fingerprint of cardiac tissue, exploiting a Langendorff-perfused isolated rat heart model to induce physiological insults to the heart, with a view to understanding how metabolic alterations affect the autofluorescence signals. Changes in the autofluorescence intensity and lifetime signatures associated with reduced nicotinamide adenine dinucleotide (phosphate) (NAD(P)H) and flavin adenine dinucleotide (FAD) were characterized during oxygen- or glucose-depletion protocols. Results suggest that both NAD(P)H and FAD autofluorescence intensity and lifetime parameters are sensitive to changes in the metabolic state of the heart owing to oxygen deprivation. We also observed changes in NAD(P)H fluorescence intensity and FAD lifetime parameter on reperfusion of oxygen, which might provide information on reperfusion injury, and permanent tissue damage or changes to the tissue during recovery from oxygen deprivation. We found that changes in the autofluorescence signature following glucose-depletion are, in general, less pronounced, and most clearly visible in NAD(P)H related parameters. Overall, the results reported in this investigation can serve as baseline for future investigations of cardiac tissue involving autofluorescence measurements.</p>
Data from: Billeci et al. "Patient-specific seizure prediction based on heart rate variability and recurrence quantification analysis"
<p>Dataset of electrocardiogram and electroencephalogram signals (.edf) acquired in epileptic patients (N=15).</p> <p>All the patients were long-term monitored with a Video-EEG, with electrodes arranged on the basis of the international 10-20 system, and with ECG. ECG was measured simultaneously with a sampling rate of 512 Hz.</p> <p>Each data include a descriptor file (.txt) containing all the information related to the acquisition: data, registration start (time), registration end (time), seizure/s start, seizure/s end and the electrodes involved at the seizure onset.</p> <p> </p>
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