Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
45
datasets available to search
ShareScore release 0.9.0
Dataset results
45 results for “PPG”
QUMPHY MIMIC IV Waveform Database PPG formatted
<p>Derivative of the <a href="https://physionet.org/content/mimic4wdb/0.1.0/">MIMIC IV Waveform Database</a> formatted to be suitable for machine learning.</p> <p><strong>Formatting</strong></p> <p>All records are split into intervals of roughly 60 seconds. The parameter values are averaged over each 60 second interval. The PPG signal data are unprocessed, i.e. as in the original dataset. Intervals with PPG signals containing missing data or large constant data are excluded. PPG signals and signal times are truncated to have the same amount of data points for all records.</p> <p>Formatted data are split into 3 different file types, namely <code>*_n.csv</code> containing the averaged parameter values, <code>*_s.npy</code> containing PPG signal data and <code>*_t.npy</code> containing the respective signal measurement times. Moreover, formatted data are split into <code>trainXX_*</code>, <code>validation_*</code> and <code>test_*</code> data files, where the training data <code>trainXX_*</code> are split into multiple files for easier handling.</p> <p>This dataset was created using the following code: <a href="https://gitlab.com/qumphy/wp1-benchmark-data-conversion">https://gitlab.com/qumphy/wp1-benchmark-data-conversion</a></p> <p><strong>Funding</strong></p> <p>The creation of this dataset has been supported by the European Partnership on Metrology programme 22HLT01 QUMPHY. This project (22HTL01 QUMPHY) has received funding from the EMPIR programme cofinanced by the Participating States and from the European Union’s Horizon 2020 research and innovation programme.</p>
Assessment of Non-Invasive Blood Pressure Prediction from PPG and rPPG Signals Using Deep Learning
<p>This dataset is a subset of the MIMIC-III dataset used for non-invasive blood pressure prediction. PPG and ABP data were divided into windows of 7s length (875 data points). Systolic and diastolic blood pressure values were derived from the ABP windows. Each sample of the dataset consists of a PPG signal and blood pressure values as well as a unique subject identifier. The file consists of three datasets:</p> <ul> <li>PPG: PPG data of size 905,400 x 875</li> <li>label: BP data of size 905,400 x 2</li> <li>subject_idx: subject affiliation of each sample (size 905,400 x 1)</li> </ul> <p>Furthermore, this submission contains the following models:</p> <ul> <li>AlexNet</li> <li>ResNet50</li> <li>LSTM</li> <li>Architecture published by Slapnicar et al. 2019</li> </ul> <p>The architectures were trained using a non-mixed dataset derived from the MIMIC-III waveform database. Samples were divided between training, validation and test set based on their subject affiliation preventing contamination of validation and test sets with samples from subjects used for training.</p>
Synthetic realistic noise-corrupted PPG database and noise generator for the evaluation of PPG denoising and delineation algorithms
<p><strong>Overview </strong></p> <p>This database is meant to evaluate the performance of denoising and delineation algorithms for PPG signals affected by noise. The noise generator allows applying the algorithms under test to an artificially corrupted reference PPG signal and comparing its output to the output obtained with the original signal. Moreover, the noise generator can produce artifacts of variable intensities, permitting the evaluation of the algorithms' performance against different noise levels. The reference signal is a PPG sample of a healthy subject at rest during a relaxing session.</p> <p> </p> <p><strong>Database</strong></p> <p>The database includes 1 recording of 72 seconds of synchronous PPG and ECG signals sampled at 250 Hz using a Medicom device, ABP-10 module (Medicom MTD Ltd., Russia). It was collected from a healthy subject during an induced relaxation by guided autogenic relaxation. For more information about the data collection, please refer to the following publication: <a href="https://pubmed.ncbi.nlm.nih.gov/30094756/">https://pubmed.ncbi.nlm.nih.gov/30094756/</a></p> <p>In addition, PPG signals corrupted by the noise generator at different levels are also included in the database.</p> <p> </p> <p><strong>Realistic noise generator</strong></p> <p>Motion Artifacts in PPG signals generally appear in the form of sudden spikes (in correspondence to the subject's movement) and slowly varying offsets (baseline wander) due to the changes in distance between the skin and the sensor after every sudden movement. For this reason, conventional noise generators — using random noise drawn from different distributions such as Gaussian or Poissonian — do not allow to properly evaluate the algorithm's performance, as they can only provide unrealistic noises compared to the one commonly found in PPG signals. To overcome this issue, we designed a more realistic synthetic noise generator that can simulate those two behaviors, enabling us to corrupt a reference signal with different noise levels. The details about noise generation are available in the reference paper.</p> <p> </p> <p><strong>Data Files</strong></p> <p>The reference PPG signal can be found in <em>Datasets\GoodSignals\PPG</em> and the simultaneously acquired ECG in <em>Datasets\GoodSignals\ECG</em>. The folder <em>Datasets\NoisySignals</em> contains 340 noisy PPG signals affected by different levels of noise. The names describe the intensity of the noise (evaluated in terms of the standard deviation of the random noise used as input for the noise generator, see reference paper). Five noisy signals are produced for every noise level by running the noise generator with five random seeds each (for noise generation).</p> <p>Name convention: <em>ppg_stdx_y</em> denotes the y-th noisy PPG signal produced using a noise with a standard deviation of x.</p> <p><em>Datasets\BPMs</em> contains the ground truth for the heart-rate estimation computed in windows of 8s with an overlap of 2s.</p> <p><strong>Code</strong></p> <p>The folder <em>Code </em>contains the MATLAB scripts to generate the noisy files by generating the realistic noise with the function noiseGenerator.</p> <p><strong>When referencing this material, please cite:</strong></p> <p>Masinelli, G.; Dell'Agnola, F.; Valdés, A.A.; Atienza, D. SPARE: A Spectral Peak Recovery Algorithm for PPG Signals Pulsewave Reconstruction in Multimodal Wearable Devices. <em>Sensors</em> <strong>2021</strong>, <em>21</em>, 2725. <a href="https://doi.org/10.3390/s21082725">https://doi.org/10.3390/s21082725</a></p>
HUSM and GMicro: PPG Peak detection
<p>Photoplethysmography (PPG) is a non-invasive technique that measures changes in blood flow volume through optical means. Accurate detection of peaks in the PPG waveform allows the extraction of the heart rate variability (HRV), a recognized indicator for the health of the cardiac and autonomic nervous systems. We make available a novel dataset comprising 66 minutes of PPG recordings from healthy individuals and patients with cardiopulmonary diseases, in three distinct phases of an acquisition protocol, with annotations for every systolic peak present.</p> <p>The dataset composition and organization are detailed in the paper that acompanies the dataset.</p>
Sample Raw (PPG, Accel, and Gyro) and Processed (steps, calories, sleep, HR, HRV, SPO2, Respiratory Rate, R-R) data over 24 hours
<p>Over the course of 24 hours, we collected raw (Photoplethysmography (PPG), Acceleration, and Gyro) and processed (steps, calories, sleep, HR, HRV, SPO2, Respiratory Rate, R-R) data samples. Biostrap approaches health insights from a data-driven perspective. Our clinical-grade hardware enables users to accurately track SpO2, HRV, RHR, and a variety of other biometrics with confidence.</p>
PPG Diary Pilot Study 1 Data
<p>The following data are provided from the PPG Diary 1 Study. <em>Definition: PPG - photoplethysmogram</em></p> <p> </p> <p><strong>Versions:</strong></p> <ul> <li><strong>Preliminary Subsets v.0.1: </strong>Three subsets are provided: (i) a one-hour recording of simultaneous red and infrared PPG signals; (ii) a one-minute recording of infrared PPG signal; (iii) a recording of a single pulse wave, acquired using infrared PPG. Each recording was acquired from the same subject during sleep. The recordings are provided in Matlab ® format.</li> <li><strong>v.1.1 - Complete Dataset: </strong>The complete dataset is provided in the Matlab ® file named <em>ppg_diary_pilot_conv_data.mat</em>. The contents of the file are described in the README file named <em>ppg_diary_pilot_study_data_README.txt </em>. In addition, the Matlab ® codes used to analyse the dataset are also provided: <em>ppg_diary_pilot1_analysis.m</em> (which is the main function), and <em>PulseAnalyse </em>(v.1.2beta, which is called by the main function). Instructions on how to use the codes to analyse the dataset are provided <a href="https://github.com/peterhcharlton/ppg-diary/blob/master/ppg-diary_v1.0/README.md">here</a>. </li> <li><strong>v.1.2 - including the Submitted Manuscript</strong>: The submitted manuscript is available <a href="https://zenodo.org/record/5211472/files/PCharlton%20et%20al%20-%20Acquiring%20wearable%20PPG%20data%20%28submitted%20manuscript%29.docx?download=1">here</a>.</li> </ul> <p> </p> <p><strong>Further Information</strong></p> <p>Further information of the PPG Diary Project, and the data provided here, is available at: <a href="https://peterhcharlton.github.io/ppg-diary/">https://peterhcharlton.github.io/ppg-diary/</a></p>
Blood pressure monitoring during anesthesia induction using PPG morphology features and machine learning
<p>PPG-BP dataset of forty patients undergoing general anesthesia, as described in the corresponding journal publication at PLOS ONE (10.1371/journal.pone.0279419).</p> <p>When using this data, please cite the corresponding journal publication.</p> <p> </p>
Post Prandial Glucose (PPG) Study of Empagliflozin in Japanese Patients With Type 2 Diabetes Mellitus
ClinicalTrials.gov study NCT01947855. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Validation Study of the Watch HWA09 and Its Softwares ECG-SW1 and PPG-SW1 for the Detection of Atrial Fibrillation
ClinicalTrials.gov study NCT04351386. IPD Sharing: NO. Countries: 1. Publications: 1.
A PPG Sensor-Based Feedback Intervention for Heavy Drinking Young Adults
ClinicalTrials.gov study NCT05090995. IPD Sharing: NO. Countries: 1. Publications: 67.
PPG to Predict Ejection Fraction and Other Echographic Data in the General Population
ClinicalTrials.gov study NCT04843371. IPD Sharing: Not stated. Countries: 1. Publications: 9.
Evaluation of the Efficacy and Safety of Portal Pressure Gradient (PPG) Measurement Guided by Endoscopic Ultrasound (EUS) in the Therapeutic Algorithm of Patients with Liver Cirrhosis.
ClinicalTrials.gov study NCT06664307. IPD Sharing: NO. Countries: 1. Publications: 1.
Determine AF Burden With PPG Trial - Detection and Quantification of Episodes of Atrial Fibrillation
ClinicalTrials.gov study NCT04563572. IPD Sharing: NO. Countries: 1. Publications: 1.
Pullback Pressure Gradient (PPG) Global Registry
ClinicalTrials.gov study NCT04789317. IPD Sharing: NO. Countries: 12. Publications: 5.
Comparing PPG-based Blood Pressure Measurement to the Gold Standard Manometer
ClinicalTrials.gov study NCT03810586. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Comparing Cardiac Output Measurements Using a Wearable, Wireless, Non-invasive PPG-Based Device to a Swan Ganz Catheter
ClinicalTrials.gov study NCT04955184. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Hearables: Ear-ECG and PPG for Detection of Cardiac Arrhythmias
ClinicalTrials.gov study NCT06667258. IPD Sharing: NO. Countries: 1. Publications: 1.
A Study on the Effectiveness of the Application of an Artificial Intelligence Algorithm for Calibrating PPG With ECG to Improve the Accuracy of Atrial Fibrillation Burden Estimation
ClinicalTrials.gov study NCT06552468. IPD Sharing: NO. Countries: 1. Publications: 0.
PPG Project 3 - PET/MRI of the Brain-hematopoiesis-atherosclerosis Axis in PTSD Patients
ClinicalTrials.gov study NCT03279393. IPD Sharing: YES. Countries: 1. Publications: 15.
Validation of Software for Assessment of Atrial Fibrillation From PPG Data Acquired by a Wearable Smartwatch
ClinicalTrials.gov study NCT04380415. IPD Sharing: NO. Countries: 1. Publications: 2.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
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.