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ShareScore release 0.9.0
Dataset results
8 results for “electrodermal activity”
Wrist-worn sensor validation for heart rate variability and electrodermal activity detection in a stressful driving environment
<p>The current dataset contributes to assess the accuracy of the Empatica 4 (E4) wristband for the detection of heart rate variability (HRV) and electrodermal activity (EDA) metrics in stress-inducing conditions and growing-risk driving scenarios. Heart Rate Variability (HRV) and ElectroDermal Activity (EDA) signals were recorded over six experimental conditions (i.e., Baseline, Video Clip, Scream, No Risk Driving, Low-Risk Driving, and High-Risk Driving) and by means of two measurement systems: the E4 device and a gold standard system. The raw quality of the physiological signals was enhanced by means of robust semi-automatic reconstruction algorithms. Heart Rate Variability time-domain parameters showed high accuracy in motion-free experimental conditions, while Heart Rate Variability frequency-domain parameters reported sufficient accuracy in almost every experimental condition.</p>
Measurement of Osteoarthritic Patient Pain Through Electrodermal Activity Signals
ClinicalTrials.gov study NCT06701461. IPD Sharing: NO. Countries: 1. Publications: 2.
Reliability Study Using Electrodermal Activity to Measure Sensory Processing in Children With and Without Autism Spectrum Disorder
ClinicalTrials.gov study NCT02646696. IPD Sharing: NO. Countries: 0. Publications: 3.
Ambulatory Electrodermal Activity Measurements as Part of Identification and Prevention of Job Burnout
ClinicalTrials.gov study NCT05317793. IPD Sharing: YES. Countries: 1. Publications: 0.
Detection of Electrodermal Activity in Pain 2
ClinicalTrials.gov study NCT06677593. IPD Sharing: NO. Countries: 1. Publications: 0.
Electrodermal Activity and Respiration in Patients Treated With Spinal Cord Stimulation
ClinicalTrials.gov study NCT04121104. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Detection of Electrodermal Activity in Pain 1
ClinicalTrials.gov study NCT06678217. IPD Sharing: NO. Countries: 1. Publications: 0.
Electrodermal Activity artifact correction BEnchmark (EDABE)
<p>"Electrodermal Activity artifact correction BEnchmark" (EDABE) is a dataset for training and testing artifact recognition and correction models to automatically remove major artifacts in electrodermal activity (EDA) signals. It is the first public benchmark to compare methods.</p> <p>EDABE contains a total of 74.46 h of EDA recording affected by hand and body motion artifacts from 43 subjects. It is divided into a training set with 33 subjects (56.27 h), and test set with 10 subjects (18.19 h). The data was collected using a Shimmer3 GSR+ Unit at 128 Hz.</p> <p>The dataset is used to develop a fully automatic pipeline that emulates the manual correction done by the expert, providing a final clean signal. The paper that describe the pipeline is currently in a peer-review process.</p> <p>Each file includes in the filename the user_id and the expert that correct the signal. In addition, the file includes the signal with the following variables:</p> <ul> <li>time: timestamp of the signal.</li> <li>rawdata: raw data obtained by Shimmer3 GSR+ Unit.</li> <li>cleandata: reconstructed clean signal performed by a human expert.</li> <li>binarytarget: label of each sample as artefact or no artifact.</li> <li>signal_automatic: automatic cleaning of the signal performed by the automatic pipeline.</li> <li>predArtifacts: label predicted by the automatic cleaning pipeline.</li> <li>postProcessedPredArtifacts: label predicted by the automatic cleaning pipeline after postprocessing.</li> </ul>
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OpenNeuro
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