Automatic identification of artifacts in electrodermal activity data

Sara Taylor; Natasha Jaques; Weixuan Chen; Szymon Fedor; Akane Sano; Rosalind Picard

doi:10.1109/EMBC.2015.7318762

Automatic identification of artifacts in electrodermal activity data

Sara Taylor, Natasha Jaques, Weixuan Chen, Szymon Fedor, Akane Sano, Rosalind Picard

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

186 Scopus citations

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.

Original language	English (US)
Title of host publication	2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1934-1937
Number of pages	4
ISBN (Electronic)	9781424492718
DOIs	https://doi.org/10.1109/EMBC.2015.7318762
State	Published - Nov 4 2015
Event	37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015 - Milan, Italy Duration: Aug 25 2015 → Aug 29 2015

Publication series

Name	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Volume	2015-November
ISSN (Print)	1557-170X

Other

Other	37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015
Country/Territory	Italy
City	Milan
Period	8/25/15 → 8/29/15

ASJC Scopus subject areas

Signal Processing
Biomedical Engineering
Computer Vision and Pattern Recognition
Health Informatics

Access to Document

10.1109/EMBC.2015.7318762

Cite this

Taylor, S., Jaques, N., Chen, W., Fedor, S., Sano, A., & Picard, R. (2015). Automatic identification of artifacts in electrodermal activity data. In 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015 (pp. 1934-1937). Article 7318762 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS; Vol. 2015-November). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC.2015.7318762

Automatic identification of artifacts in electrodermal activity data. / Taylor, Sara; Jaques, Natasha; Chen, Weixuan et al.
2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015. Institute of Electrical and Electronics Engineers Inc., 2015. p. 1934-1937 7318762 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS; Vol. 2015-November).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Taylor, S, Jaques, N, Chen, W, Fedor, S, Sano, A & Picard, R 2015, Automatic identification of artifacts in electrodermal activity data. in 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015., 7318762, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, vol. 2015-November, Institute of Electrical and Electronics Engineers Inc., pp. 1934-1937, 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015, Milan, Italy, 8/25/15. https://doi.org/10.1109/EMBC.2015.7318762

Taylor S, Jaques N, Chen W, Fedor S, Sano A, Picard R. Automatic identification of artifacts in electrodermal activity data. In 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015. Institute of Electrical and Electronics Engineers Inc. 2015. p. 1934-1937. 7318762. (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). doi: 10.1109/EMBC.2015.7318762

Taylor, Sara ; Jaques, Natasha ; Chen, Weixuan et al. / Automatic identification of artifacts in electrodermal activity data. 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015. Institute of Electrical and Electronics Engineers Inc., 2015. pp. 1934-1937 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

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AB - 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.

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Automatic identification of artifacts in electrodermal activity data

Abstract

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