title: Automatic identification of artifacts in electrodermal activity data.
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.
authors.forename: Sara
authors.initials: S
authors.lastname: Taylor
authors.forename: Natasha
authors.initials: N
authors.lastname: Jaques
authors.forename: Weixuan
authors.initials: W
authors.lastname: Chen
authors.forename: Szymon
authors.initials: S
authors.lastname: Fedor
authors.forename: Akane
authors.initials: A
authors.lastname: Sano
authors.forename: Rosalind
authors.initials: R
authors.lastname: Picard
mesh_terms: D000465:Algorithms; D005712:Galvanic Skin Response; D006801:Humans; D000069550:Machine Learning; D012815:Signal Processing, Computer-Assisted