Automatic identification of artifacts in electrodermal activity data
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 fo...
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| Format: | Article |
| Language: | en_US |
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Institute of Electrical and Electronics Engineers (IEEE)
2016
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| Online Access: | http://hdl.handle.net/1721.1/103781 https://orcid.org/0000-0001-9550-2553 https://orcid.org/0000-0002-8413-9469 https://orcid.org/0000-0003-4484-8946 https://orcid.org/0000-0002-5661-0022 https://orcid.org/0000-0002-9857-0188 https://orcid.org/0000-0003-4133-9230 |
| _version_ | 1811070546895437824 |
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| author | Taylor, Sara Ann Jaques, Natasha Mary Chen, Weixuan Fedor, Szymon Sano, Akane Picard, Rosalind W. |
| author2 | Massachusetts Institute of Technology. Media Laboratory |
| author_facet | Massachusetts Institute of Technology. Media Laboratory Taylor, Sara Ann Jaques, Natasha Mary Chen, Weixuan Fedor, Szymon Sano, Akane Picard, Rosalind W. |
| author_sort | Taylor, Sara Ann |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T08:37:46Z |
| format | Article |
| id | mit-1721.1/103781 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T08:37:46Z |
| publishDate | 2016 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| record_format | dspace |
| spelling | mit-1721.1/1037812022-09-23T13:26:31Z Automatic identification of artifacts in electrodermal activity data Taylor, Sara Ann Jaques, Natasha Mary Chen, Weixuan Fedor, Szymon Sano, Akane Picard, Rosalind W. Massachusetts Institute of Technology. Media Laboratory Program in Media Arts and Sciences (Massachusetts Institute of Technology) Taylor, Sara Ann Jaques, Natasha Mary Chen, Weixuan Fedor, Szymon Sano, Akane Picard, Rosalind W. 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) 2016-07-20T19:07:13Z 2016-07-20T19:07:13Z 2015-08 Article http://purl.org/eprint/type/ConferencePaper 978-1-4244-9271-8 INSPEC Accession Number: 15584636 http://hdl.handle.net/1721.1/103781 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. https://orcid.org/0000-0001-9550-2553 https://orcid.org/0000-0002-8413-9469 https://orcid.org/0000-0003-4484-8946 https://orcid.org/0000-0002-5661-0022 https://orcid.org/0000-0002-9857-0188 https://orcid.org/0000-0003-4133-9230 en_US http://dx.doi.org/10.1109/EMBC.2015.7318762 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Institute of Electrical and Electronics Engineers (IEEE) MIT web domain |
| spellingShingle | Taylor, Sara Ann Jaques, Natasha Mary Chen, Weixuan Fedor, Szymon Sano, Akane Picard, Rosalind W. Automatic identification of artifacts in electrodermal activity data |
| title | Automatic identification of artifacts in electrodermal activity data |
| title_full | Automatic identification of artifacts in electrodermal activity data |
| title_fullStr | Automatic identification of artifacts in electrodermal activity data |
| title_full_unstemmed | Automatic identification of artifacts in electrodermal activity data |
| title_short | Automatic identification of artifacts in electrodermal activity data |
| title_sort | automatic identification of artifacts in electrodermal activity data |
| url | http://hdl.handle.net/1721.1/103781 https://orcid.org/0000-0001-9550-2553 https://orcid.org/0000-0002-8413-9469 https://orcid.org/0000-0003-4484-8946 https://orcid.org/0000-0002-5661-0022 https://orcid.org/0000-0002-9857-0188 https://orcid.org/0000-0003-4133-9230 |
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