Automatic identification of artifacts in electrodermal activity data

• Main Authors: Taylor, Sara Ann (Contributor), Jaques, Natasha Mary (Contributor), Chen, Weixuan (Contributor), Fedor, Szymon (Contributor), Sano, Akane (Contributor), Picard, Rosalind W. (Contributor)
• Other Authors: Massachusetts Institute of Technology. Media Laboratory (Contributor), Program in Media Arts and Sciences (Massachusetts Institute of Technology) (Contributor)
• Format: Article
• Language: English
• Published: Institute of Electrical and Electronics Engineers (IEEE), 2016-07-20T19:07:13Z.
• Subjects: Article
• Online Access: Get fulltext

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 Media Lab Consortium
Samsung (Firm)
National Institutes of Health (U.S.) (NIH grant R01GM105018)
Natural Sciences and Engineering Research Council of Canada
Seventh Framework Programme (European Commission) (People Programme (Marie Curie Actions), FP7/2007-2013/ under REA grant agreement #327702)