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96 results for “pulse wave”
Dataset used in the publication entitled "Decomposition by Approximation with Pulse Waves Allowing Further Research on Sources of Voltage Fluctuations"
<p>Dataset obtained from experimental research carried out in the prepared laboratory setup. Based on the dataset, the proposed new decomposition method by approximation with pulse waves has been validated in the publication: Kuwałek P., Decomposition by Approximation with Pulse Waves Allowing Further Research on Sources of Voltage Fluctuations. The description of the prepared laboratory setup is presented in this publication. The research results are part of the work under the project entitled "Voltage fluctuation diagnostic focused on identification and localization disturbing loads in power grids" funded by the National Science Centre, Poland - 2021/41/N/ST7/00397.</p>
Simulated Arterial Pulse Waves Database (preliminary version)
<p> </p> <p><em>This provides a brief overview of the database. Further details are provided at: <a href="https://peterhcharlton.github.io/pwdb/ppwdb.html">https://peterhcharlton.github.io/pwdb/ppwdb.html</a></em></p> <p><strong>Background:</strong> The shape of the arterial pulse wave (PW) is a rich source of information on cardiovascular (CV) health, since it is influenced by both the heart and the vasculature. Consequently, many algorithms have been proposed to estimate clinical parameters from PWs. However, it is difficult and costly to acquire comprehensive datasets with which to assess their performance. We are aiming to address this difficulty by creating a database of simulated PWs under a range of CV conditions, representative of a healthy population. The database provided here is an initial version which has already been used to gain some novel insights into haemodynamics.</p> <p><strong>Methods:</strong> Baseline PWs were simulated using 1D computational modelling. CV model parameters were varied across normal healthy ranges to simulate a sample of subjects for each age decade from 25 to 75 years. The model was extended to simulate photoplethysmographic (PPG) PWs at common measurement sites, in addition to the pressure (ABP), flow rate (Q), flow velocity (U) and diameter (D) PWs produced by the model.</p> <p><strong>Validation:</strong> The database was verified by comparing simulated PWs with in vivo PWs. Good agreement was observed, with age-related changes in blood pressure and wave morphology well reproduced.</p> <p><strong>Conclusion:</strong> This database is a valuable resource for development and pre-clinical assessment of PW analysis algorithms. It is particularly useful because it contains several types of PWs at multiple measurement sites, and the exact CV conditions which generated each PW are known.</p> <p><strong>Future work:</strong> However, there are two limitations: (i) the database does not exhibit the wide variation in cardiovascular properties observed across a population sample; and (ii) the methods used to model changes with age have been improved since creating this initial version. Therefore, we are currently creating a more comprehensive database which addresses these limitations.</p> <p><strong>Accompanying Presentation:</strong> This database was originally presented at the BioMedEng18 Conference. The presentation describing the methods for creating the database, and providing an introduction to the database, is available at: <a href="https://www.youtube.com/watch?v=X8aPZFs8c08">https://www.youtube.com/watch?v=X8aPZFs8c08</a> . The accompanying abstract is available <a href="https://kclpure.kcl.ac.uk/portal/en/publications/a-database-for-the-development-of-pulse-wave-analysis-algorithms(d14a02f7-ae79-4761-b17e-621d45094591).html">here</a>.</p> <p><strong>Accompanying Manual: </strong>Further information on how to use the PWDB datasets, including this preliminary dataset, are provided in the <a href="https://github.com/peterhcharlton/pwdb/wiki">user manual</a>. Further details on the contents of the dataset files are available <a href="https://github.com/peterhcharlton/pwdb/wiki/Using-the-Pulse-Wave-Database">here</a>.</p> <p><strong>Citation: </strong>When using this dataset please cite <a href="https://kclpure.kcl.ac.uk/portal/en/publications/modelling-arterial-pulse-wave-propagation-during-healthy-ageing(6579ac0c-f092-4ab5-9dc6-2a7aeda6c78d).html">this publication</a>:</p> <p><a href="https://kclpure.kcl.ac.uk/portal/en/publications/modelling-arterial-pulse-wave-propagation-during-healthy-ageing(6579ac0c-f092-4ab5-9dc6-2a7aeda6c78d).html">Charlton P.H. <em>et al.</em> <strong>Modelling arterial pulse wave propagation during healthy ageing</strong>, In <em>World Congress of Biomechanics 2018</em>, Dublin, Ireland, 2018.</a></p> <p><strong>Version History:</strong></p> <p>- v.1.0: Originally uploaded to PhysioNet. This is the version which was used in the accompanying presentation.</p> <p>- v.2.0: The initial upload to this DOI. The database was curated using the <a href="https://doi.org/10.5281/zenodo.3271512">PWDB Algorithms</a> v.0.1.1. It differs slightly from the originally reported version in that: (i) the augmentation pressure and index were calculated at the aortic root rather than the carotid artery.</p> <p><em>Text adapted from: Charlton P.H. et al., '<a href="https://kclpure.kcl.ac.uk/portal/en/publications/a-database-for-the-development-of-pulse-wave-analysis-algorithms(d14a02f7-ae79-4761-b17e-621d45094591).html">A database for the development of pulse wave analysis algorithms',</a> BioMedEng18, London, 2018.</em></p> <p> </p>
Experimental Database of deterministic wave prediction built from synchronous measurements from an X-band pulse radar and met-ocean sensors deployed on the Floatgen floating wind turbine and its vicinity on SEM-REV test site.
<p>This dataset is a deliverable of the FLOATECH project, funded under the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101007142.<br> The aim of this dataset is a result of the field experiments carried out at the Floatgen FOWT located at the SEM-REV test site.</p>
Machine learning-based pulse wave analysis for classification of circle of Willis topology: an in silico study with 30,618 virtual subjects (database: Complete CoW)
<p>This repository contains the dataset for the complete CoW described in the article with the same name. MATLAB and Python codes for post-processing the dataset and the code for training and testing all machine learning models using the open-source library TensorFlow 2.12, the Keras application programming interface, and the Scikit-learn Python package can be found in here (<a href="https://zenodo.org/records/12519322" target="_blank" rel="noopener">https://zenodo.org/records/12519322</a>).</p>
2D Array of Pulse Coupled Oscillators Driving Cilia to Demonstrate Metachronal Waves
<p>The video demonstrates metachornal waves from an 2D array of delay locked pulse coupled oscillators, each mapping to a cilia.<br>Within the triangle structure, the oscillators are coupled to their nearest neighbors.</p>
Pulse Wave Database (PWDB): A database of arterial pulse waves representative of healthy adults
<p><strong>Overview</strong></p> <p>This database of simulated arterial pulse waves is designed to be representative of a sample of pulse waves measured from healthy adults. It contains pulse waves for 4,374 virtual subjects, aged from 25-75 years old (in 10 year increments). The database contains a baseline set of pulse waves for each of the six age groups, created using cardiovascular properties (such as heart rate and arterial stiffness) which are representative of healthy subjects at each age group. It also contains 728 further virtual subjects at each age group, in which each of the cardiovascular properties are varied within normal ranges. This allows for extensive <em>in silico</em> analyses of haemodynamics and the performance of pulse wave analysis algorithms.</p> <p><strong>Data Description</strong></p> <p>The database contains the following waves:</p> <ul> <li>arterial flow velocity (U),</li> <li>luminal area (A),</li> <li>pressure (P), and</li> <li>photoplethysmogram (PPG).</li> </ul> <p>These pulse waves are provided at a range of measurement sites, including:</p> <ul> <li>aorta (ascending and descending)</li> <li>carotid artery</li> <li>brachial artery</li> <li>radial artery</li> <li>finger</li> <li>femoral artery</li> </ul> <p>The data are available in three formats: Matlab, CSV and WaveForm Database (WFDB) format. Further details of the formatting and contents of each file are available at: <a href="https://github.com/peterhcharlton/pwdb/wiki/Using-the-Pulse-Wave-Database">https://github.com/peterhcharlton/pwdb/wiki/Using-the-Pulse-Wave-Database</a></p> <p><strong>Accompanying Publication</strong></p> <p>The database is described in the following publication:</p> <p><a href="https://peterhcharlton.github.io/pwdb/pwdb_article.html">Charlton P.H., Mariscal Harana, J., Vennin, S., Li, Y., Chowienczyk, P. & Alastruey, J., “Modelling arterial pulse waves in healthy ageing: a database for in silico evaluation of haemodynamics and pulse wave indices,”</a> [under review]</p> <p>Please cite this publication when using the database.</p> <p><strong>Further Information</strong></p> <p>Further information on the Pulse Wave Database project can be found at: <a href="https://peterhcharlton.github.io/pwdb/"><em>https://peterhcharlton.github.io/pwdb/</em></a></p> <p><strong>Version History</strong></p> <p> </p> <p><strong>Version 0.1.0 : provided for peer review of "Modelling arterial pulse waves in healthy ageing: a database for in silico evaluation of haemodynamics and pulse wave indices"</strong></p> <p><strong>Version 0.2.0 : provided for peer review of "Modelling arterial pulse waves in healthy ageing: a database for in silico evaluation of haemodynamics and pulse wave indices"</strong></p>
Degenerate four wave mixing measurements - pulsed laser
<p>Transmission from a photonic molecule, measured with photodetector attached to a DAQ and OSA, as a function of detuning, for various input powers. Pulsed laser.</p>
Correlation Between EIT-based Pulse Wave Method for Pulmonary Perfusion Monitoring and Pulmonary Artery Catheter-based Stroke Volume Measurement
ClinicalTrials.gov study NCT07385963. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Correlation between estimated pulse wave velocity values from two equations in healthy and under cardiovascular risk populations
<p><strong>Introduction </strong><strong>: </strong>Equations can calculate pulse wave velocity (ePWV) from blood pressure values (BP) and age. The ePWV predicts cardiovascular events beyond carotid-femoral PWV. We aimed to evaluate the correlation between four different equations to calculate ePWV.</p> <p><strong>Methods: </strong>The ePWV was estimated utilizing mean BP (MBP) from office BP (MBP<sub>OBP</sub>) or 24-hour ambulatory BP (MBP<sub>24-hBP</sub>). We separated the whole sample into two groups: individuals with risk factors and healthy individuals. The e-PWV was calculated as follows: </p> <p>We calculated the concordance correlation coefficient (Pc) between e1-PWV<sub>OBP</sub> vs e2-PWV<sub>OBP</sub>, e1-PWV<sub>24-hBP</sub> vs e2-PWV<sub>24-hBP</sub>, and mean values of e1-PWV<sub>OBP</sub>, e2-PWV<sub>OBP</sub>, e1-PWV<sub>24-hBP, </sub>and e2-PWV<sub>24-hBP </sub>. The multilevel regression model determined how much the ePWVs are influenced by age and MBP values.</p> <p><strong>Results:</strong> We analyzed data from 1541 individuals; 1374 ones with risk factors and 167 healthy ones. The values are presented for the entire sample, for risk-factor patients and for healthy individuals, respectively. The correlation between e1-PWV<sub>OBP</sub> with e2-PWV<sub>OBP</sub> and e1-PWV<sub>24-hBP </sub>with e2-PWV<sub>24-hBP</sub> was almost perfect. The Pc for e1-PWV<sub>OBP</sub> vs e2-PWV<sub>OBP</sub> was 0.996 (0.995-0.996), 0.996 (0.995-0.996), and 0.994 (0.992-0.995); furthermore, it was 0.994 (0.993-0.995), 0.994 (0.994-0.995), 0.987 (0.983-0.990) to the e1-PWV<sub>24-hBP </sub>vs e2-PWV<sub>24-hBP</sub>. There were no significant differences between mean values (m/s) for e1-PWV<sub>OBP</sub> vs e2-PWV<sub>OBP</sub> 8.98±1.9 vs 8.97±1.8; p=0.88, 9.14±1.8 vs 9.13±1.8; p=0.88, and 7.57±1.3 vs 7.65±1.3; p=0.5; mean values are also similar for e1-PWV<sub>24-hBP </sub>vs e2-PWV<sub>24-hBP</sub>, 8.36±1.7 vs 8.46±1.6; p=0.09, 8.50±1.7 vs 8.58±1.7; p=0.21 and 7.26±1.3 vs 7.39±1.2; p=0.34. The multiple linear regression showed that age, MBP, and age² predicted more than 99.5% of all four e-PWV.</p> <p><strong>Conclusion: </strong>Our data presents a nearly perfect correlation between the values of two equations to calculate the estimated PWV, whether utilizing office or ambulatory blood pressure.</p>
Machine learning-based pulse wave analysis for classification of circle of Willis topology: an in silico study with 30,618 virtual subjects (database: Missing ACoA)
<p>This repository contains the dataset for the Missing ACoA described in the article with the same name. MATLAB and Python codes for post-processing the dataset and the code for training and testing all machine learning models using the open-source library TensorFlow 2.12, the Keras application programming interface, and the Scikit-learn Python package can be found in here (<a href="https://zenodo.org/records/12519322" target="_blank" rel="noopener">https://zenodo.org/records/12519322</a>).</p>
Machine learning-based pulse wave analysis for classification of circle of Willis topology: an in silico study with 30,618 virtual subjects (database:Missing PCoA)
<p>This repository contains the dataset for the Missing PCoA described in the article with the same name. MATLAB and Python codes for post-processing the dataset and the code for training and testing all machine learning models using the open-source library TensorFlow 2.12, the Keras application programming interface, and the Scikit-learn Python package can be found in here (<a href="https://zenodo.org/records/12519322" target="_blank" rel="noopener">https://zenodo.org/records/12519322</a>).</p>
Machine learning-based pulse wave analysis for classification of circle of Willis topology: an in silico study with 30,618 virtual subjects (database: Missing PCoA and PCA P1)
<p>This repository contains the dataset for the Missing PCoA and PCA P1 described in the article with the same name. MATLAB and Python codes for post-processing the dataset and the code for training and testing all machine learning models using the open-source library TensorFlow 2.12, the Keras application programming interface, and the Scikit-learn Python package can be found in here (<a href="https://zenodo.org/records/12519322" target="_blank" rel="noopener">https://zenodo.org/records/12519322</a>).</p>
Machine learning-based pulse wave analysis for classification of circle of Willis topology: an in silico study with 30,618 virtual subjects (database: Missing ACA A1)
<p>This repository contains the dataset for the Missing ACA A1 described in the article with the same name. MATLAB and Python codes for post-processing the dataset and the code for training and testing all machine learning models using the open-source library TensorFlow 2.12, the Keras application programming interface, and the Scikit-learn Python package can be found in here (<a href="https://zenodo.org/records/12519322" target="_blank" rel="noopener">https://zenodo.org/records/12519322</a>).</p>
Machine learning-based pulse wave analysis for classification of circle of Willis topology: an in silico study with 30,618 virtual subjects (database: Missing PCoAs)
<p>This repository contains the dataset for the Missing PCoAs described in the article with the same name. MATLAB and Python codes for post-processing the dataset and the code for training and testing all machine learning models using the open-source library TensorFlow 2.12, the Keras application programming interface, and the Scikit-learn Python package can be found in here (<a href="https://zenodo.org/records/12519322" target="_blank" rel="noopener">https://zenodo.org/records/12519322</a>).</p>
Pulse Wave Database (PWDB): Baseline subjects aged 25 to 75
<p><strong>The Pulse Wave Database</strong></p> <p>The <a href="https://peterhcharlton.github.io/pwdb">Pulse Wave Database (PWDB)</a> is a database of simulated arterial pulse waves designed to be representative of a sample of pulse waves measured from healthy adults. It contains pulse waves for 4,374 virtual subjects, aged from 25-75 years old (in 10 year increments). The database contains a baseline set of pulse waves for each of the six age groups, created using cardiovascular properties (such as heart rate and arterial stiffness) which are representative of healthy subjects at each age group. It also contains 728 further virtual subjects at each age group, in which each of the cardiovascular properties are varied within normal ranges. The entire database is available at DOI: <a href="https://doi.org/10.5281/zenodo.2633174">10.5281/zenodo.2633174</a> .</p> <p><strong>This dataset: baseline subjects aged 25 to 75</strong></p> <p>This dataset is a subset of the PWDB. It contains the pulse waves for the six baseline subjects aged 25 to 75 (in 10 year increments). It contains the following waves:</p> <ul> <li>arterial flow velocity (U),</li> <li>luminal area (A),</li> <li>pressure (P), and</li> <li>photoplethysmogram (PPG).</li> </ul> <p>These pulse waves are provided at a range of measurement sites, including:</p> <ul> <li>aorta (ascending and descending)</li> <li>carotid artery</li> <li>brachial artery</li> <li>radial artery</li> <li>finger</li> <li>femoral artery</li> </ul> <p>The data are available in three formats: Matlab, CSV and WaveForm Database (WFDB) format. Further details of the formatting and contents of each file are available at: <a href="https://github.com/peterhcharlton/pwdb/wiki/Using-the-Pulse-Wave-Database">https://github.com/peterhcharlton/pwdb/wiki/Using-the-Pulse-Wave-Database</a></p> <p><strong>Accompanying Publication</strong></p> <p>This is a subset of the PWDB database, which is described in the following publication:</p> <p><a href="https://peterhcharlton.github.io/pwdb/pwdb_article.html">Charlton P.H., Mariscal Harana, J., Vennin, S., Li, Y., Chowienczyk, P. & Alastruey, J., “Modelling arterial pulse waves in healthy ageing: a database for in silico evaluation of haemodynamics and pulse wave indices,”</a> [under review]</p> <p>Please cite this publication when using the database.</p> <p><strong>Further Information</strong></p> <p>Further information on the Pulse Wave Database project can be found at: <a href="https://peterhcharlton.github.io/pwdb/"><em>https://peterhcharlton.github.io/pwdb/</em></a></p> <p><strong>Version History</strong></p> <p><strong>Version 1.0 : </strong>provided for peer review of "Modelling arterial pulse waves in healthy ageing: a database for in silico evaluation of haemodynamics and pulse wave indices"</p>
Impact of CPAP Therapy in Obstructive Sleep Apnea on Parameters of Nocturnal Pulse Wave Analysis
ClinicalTrials.gov study NCT01814462. IPD Sharing: Not stated. Countries: 1. Publications: 13.
Measurement of Heart-carotid Pulse Wave Velocity (hcPWV) by Laser Doppler Vibrometry (LDV)
ClinicalTrials.gov study NCT05711693. IPD Sharing: NO. Countries: 1. Publications: 1.
Carotid-Femoral, Oscillometric and Estimated Pulse Wave Velocity
ClinicalTrials.gov study NCT06836622. IPD Sharing: NO. Countries: 1. Publications: 20.
Relationship Between Blood Pressure and Pulse Wave Velocity Measurements in Peritoneal Dialysis
ClinicalTrials.gov study NCT03607747. IPD Sharing: UNDECIDED. Countries: 1. Publications: 36.
Association of Synchronous Four-limb blOod pRessure and Pulse Wave velocIty With Cardiovascular Events
ClinicalTrials.gov study NCT03521739. IPD Sharing: NO. Countries: 1. Publications: 1.
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