Journal ArticleDOI
Automatic motion artifact detection in electrodermal activity data using machine learning
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TLDR
In this article , a machine learning framework for automatic motion artifact detection on electrodermal activity signals is presented. But the detection of motion artifacts (MA) hinders accurate analysis of EDA signals.About:
This article is published in Biomedical Signal Processing and Control.The article was published on 2022-04-01. It has received 13 citations till now. The article focuses on the topics: Artifact (error) & Computer science.read more
Citations
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Journal ArticleDOI
Comparison of Electrodermal Activity from Multiple Body Locations Based on Standard EDA Indices’ Quality and Robustness against Motion Artifact
TL;DR: This study collected EDA signals from four measurement sites during cognitive stress and induction of mild motion artifacts by walking and one-handed weightlifting and evaluated the robustness of the different body sites against motion artifacts and found that the foot EDA location was the best alternative to other sites.
Journal ArticleDOI
A Deep Convolutional Autoencoder for Automatic Motion Artifact Removal in Electrodermal Activity
TL;DR: Wang et al. as mentioned in this paper proposed a deep convolutional autoencoder (DCAE) approach for automatic motion artifacts removal in electrodermal activity (EDA) signals.
Journal ArticleDOI
An unsupervised automated paradigm for artifact removal from electrodermal activity in an uncontrolled clinical setting
TL;DR: A fully automated artifact removal framework is built to remove the heavy artifacts that resulted from the use of surgical electrocautery during the surgery and compared it to two existing state-of-the-art methods for artifact removal from EDA data.
Journal ArticleDOI
A Deep Convolutional Autoencoder for Automatic Motion Artifact Removal in Electrodermal Activity
TL;DR: This work proposes a more data-driven deep convolutional autoencoder (DCAE) for automated motion artifact removal in EDA signals and shows a promising approach which can potentially be used to remove many different types of MA.
Journal ArticleDOI
Machine Learning Techniques for Arousal Classification from Electrodermal Activity: A Systematic Review
Roberto Sánchez-Reolid,Francisco López de la Rosa,Daniel Sánchez-Reolid,María T. López,Antonio Fernández-Caballero +4 more
TL;DR: In this article , a systematic review on arousal classification based on electrodermal activity (EDA) and machine learning (ML) is presented, which has made it possible to analyse all the steps to which the EDA signals are subjected: acquisition, pre-processing, processing and feature extraction.
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