Automatic identification of artifacts in electrodermal activity data
Author(s)
Taylor, Sara Ann; Jaques, Natasha Mary; Chen, Weixuan; Fedor, Szymon; Sano, Akane; Picard, Rosalind W.; ... Show more Show less
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Recently, wearable devices have allowed for long term, ambulatory measurement of electrodermal activity (EDA). Despite the fact that ambulatory recording can be noisy, and recording artifacts can easily be mistaken for a physiological response during analysis, to date there is no automatic method for detecting artifacts. This paper describes the development of a machine learning algorithm for automatically detecting EDA artifacts, and provides an empirical evaluation of classification performance. We have encoded our results into a freely available web-based tool for artifact and peak detection.
Date issued
2015-08Department
Massachusetts Institute of Technology. Media Laboratory; Program in Media Arts and Sciences (Massachusetts Institute of Technology)Journal
2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Taylor, Sara, Natasha Jaques, Weixuan Chen, Szymon Fedor, Akane Sano, and Rosalind Picard. “Automatic Identification of Artifacts in Electrodermal Activity Data.” 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (August 2015), 25-29 Aug. 2015, Milan, Italy. pp.1934-1937.
Version: Author's final manuscript
Other identifiers
INSPEC Accession Number: 15584636
ISBN
978-1-4244-9271-8