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648 results for “heart rate”
Data Archive for "Acceleration as a proxy for energy expenditure in a facultative-soaring bird: comparing dynamic body acceleration and time-energy budgets to heart rate"
<p>Heart rate, acceleration, and respirometry data from four wild-caught gulls during climate chamber and treadmill calibration measurements (2018), as well as heart rate and acceleration data from five free-ranging gulls from a colony on Texel, NL during the breeding season (May - July, 2019). </p> <p> </p>
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>
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>
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>
Determinants of heart rate in Svalbard reindeer reveal mechanisms of seasonal energy management
<p>Seasonal energetic challenges may constrain an animal's ability to respond to changing individual and environmental conditions. Here we investigated variation in heart rate, a well-established proxy for metabolic rate, in Svalbard reindeer, a species with strong seasonal changes in foraging and metabolic activity. In 19 adult females we recorded heart rate, subcutaneous temperature and activity using biologgers. Mean heart rate more than doubled from winter to summer. Typical drivers of energy expenditure, such as reproduction and activity, explained a relatively limited amount of variation (2–6% in winter and 16–24% in summer), compared to seasonality which explained 75% of annual variation in heart rate. The relationship between heart rate and subcutaneous temperature depended on individual state via body mass, age and reproductive status, and the results suggested that peripheral heterothermy is an important pathway of energy management in both winter and summer. While the seasonal plasticity in energetics make Svalbard reindeer well-adapted to their highly seasonal environment, intraseasonal constraints on modulation of their heart rate may limit their ability to respond to severe environmental change. This study emphasizes the importance of encompassing individual state and seasonal context when studying energetics in free-living animals.</p>
Data for: Non-invasive measurements of respiration and heart rate across wildlife species using Eulerian Video Magnification of infrared thermal imagery
<p><strong>Background</strong>: An animal's metabolic rate, or energetic expenditure, both impacts and is impacted by interactions with its environment. However, techniques for obtaining measurements of metabolic rate are invasive, logistically difficult, and costly. Red-green-blue (RGB) imaging tools have been used in humans and select domestic mammals to accurately measure heart and respiration rate, as proxies of metabolic rate. The purpose of this study was to investigate if infrared thermography (IRT) coupled with Eulerian video magnification (EVM) would extend the applicability of imaging tools towards measuring vital rates in exotic wildlife species with different physical attributes.</p> <p><strong>Results</strong>: We collected IRT and RGB video of 52 total species (39 mammalian, 7 avian, 6 reptilian) from 36 taxonomic families at zoological institutions and used EVM to amplify subtle changes in temperature associated with blood flow for respiration and heart rate measurements. IRT-derived respiration and heart rates were compared to 'true' measurements determined simultaneously by expansion of the ribcage/nostrils and stethoscope readings, respectively. Sufficient temporal signals were extracted for measures of respiration rate in 36 species (85% success in mammals; 50% success in birds; 100% success in reptiles) and heart rate in 24 species (67% success in mammals; 33% success in birds; 0% success in reptiles) using IRT-EVM. Infrared-derived measurements were obtained with high accuracy (respiration rate, mean absolute error: 1.9 breaths per minute, average percent error: 4.4%; heart rate, mean absolute error: 2.6 beats per minute, average percent error: 1.3%). Thick integument and animal movement most significantly hindered successful validation.</p> <p><strong>Conclusion</strong>: The combination of IRT with EVM analysis provides a non-invasive method to assess individual animal health in zoos, with great potential to monitor wildlife metabolic indices in situ.</p>
An orally angiotensin - (1 – 7) inclusion compound reduce time to reaction in 2 stroop task and modify heart rate variability after continuous test in mountain 3 bike cyclists
<p>data for An orally angiotensin - (1 – 7) inclusion compound reduce time to reaction in 2 stroop task and modify heart rate variability after continuous test in mountain 3 bike cyclists,<br> </p> <p>Recently our group showed that hydroxypropyl β-cyclodextrin (HPβ-CD)-Angiotensin-(1-7) (HPβ-CD-Ang-[1-7]) oral formulation affects performance and decreases the perceived effort of mountain bike (MTB) athletes.</p> <p>Twenty-one male MTB practitioners were divided into a continuous protocol time trial and repeated sprint groups. Three hours before a 20-km cycling time trial or 4×30-s repeated all-out sprints on a leg cycle ergometer, the athletes received HPβ-CD-Ang-(1-7) (0.8 mg) or HPβ-CD-placebo (only HPβ-CD) oral capsules over a 7-day interval randomized crossover design. At rest and immediately after the exercise protocol, the ratings of perceived recovery and the visual analog scale were assessed, and the volunteers completed the Stroop task (ST). Heart rate variability was measured at rest and peak effort. There were no differences in the perceived variables. The ST showed that HPβ-CD-Ang-(1-7) supplementation reduced the reaction time (rest 1032±331 ms vs. after protocol 902±286 ms, p=0.05) after the continuous time trial. The withdrawal of the parasympathetic components in the peak effort to the continuous protocol was not different from that of rest in the HPβ-CD-Ang-(1-7) condition. The results are pioneering, especially in humans, but indicate that Angiotensin-(1-7) potentially affects reaction time and the parasympathetic withdrawal after continuous protocol time trial.</p>
Heart Rate Response to Atropine Doses Less Than 0.1mg IV to Anesthetized Infants
ClinicalTrials.gov study NCT01819064. IPD Sharing: NO. Countries: 1. Publications: 1.
Efficacy Study of Pacemakers to Treat Slow Heart Rate in Patients With Heart Failure
ClinicalTrials.gov study NCT02145351. IPD Sharing: NO. Countries: 1. Publications: 1.
Association Between Fluid Administration, Oxytocin Administration, and Fetal Heart Rate Changes
ClinicalTrials.gov study NCT02121184. IPD Sharing: NO. Countries: 1. Publications: 12.
Closed Loop Control in Adolescents Using Heart Rate as Exercise Indicator
ClinicalTrials.gov study NCT01945060. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Pulse Pressure and Post-epidural Fetal Heart Rate Changes
ClinicalTrials.gov study NCT02565485. IPD Sharing: Not stated. Countries: 1. Publications: 23.
Heart Rate Response to Regadenoson and Sudden Cardiac Death
ClinicalTrials.gov study NCT01842035. IPD Sharing: Not stated. Countries: 1. Publications: 5.
The Influence of Heart Rate Limitation on Exercise Tolerance in Pacemaker Patients.
ClinicalTrials.gov study NCT02247245. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Comparing Rate Response With CLS Versus Accelerometer ICD Settings in Heart Failure Patients With BIOTRONIK CRT-Ds
ClinicalTrials.gov study NCT02693262. IPD Sharing: NO. Countries: 1. Publications: 8.
A Study to Assess the Effect of Exenatide Treatment on Mean 24-Hour Heart Rate in Patients With Type 2 Diabetes
ClinicalTrials.gov study NCT00516074. IPD Sharing: Not stated. Countries: 2. Publications: 1.
Effect of Nebulized Bronchodilators on Heart Rate
ClinicalTrials.gov study NCT01151579. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Noninvasive Monitoring of Uterine Electrical Activity and Fetal Heart Rate: A New External Monitoring Device
ClinicalTrials.gov study NCT02993354. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Heart Rate Reduction in Heart Failure
ClinicalTrials.gov study NCT01178528. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Effects of Intramuscular (IM) Oxytocin on Pupil Diameter and Heart Rate Variability (HRV)
ClinicalTrials.gov study NCT04105998. IPD Sharing: NO. Countries: 1. Publications: 2.
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OpenNeuro
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