Automatic identification of artifacts in electrodermal activity data
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Picard_Automatic identification.pdf
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Author(s)
Taylor, Sara Ann
Jaques, Natasha Mary
Chen, Weixuan
Fedor, Szymon
Sano, Akane
Picard, Rosalind W.
Date Issued
August 2015
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.
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Author's final manuscript
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Media Laboratory
Program in Media Arts and Sciences (Massachusetts Institute of Technology)
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1109/EMBC.2015.7318762
