A machine learning algorithm was developed and empirically evaluated for automatically detecting electrodermal activity artifacts, resulting in a freely available web-based tool.
Provides a novel machine learning algorithm and web-based tool for automatically detecting artifacts in ambulatory 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 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.
Taylor et al. (2015) studied Electrodermal activity (EDA) artifacts. Machine learning algorithm for detecting EDA artifacts was evaluated on Classification performance of the artifact detection algorithm. A machine learning algorithm was developed and empirically evaluated for automatically detecting electrodermal activity artifacts, resulting in a freely available web-based tool.