| Literature DB >> 26736662 |
Sara Taylor, Natasha Jaques, Weixuan Chen, Szymon Fedor, Akane Sano, Rosalind Picard.
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.Entities:
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Year: 2015 PMID： 26736662 PMCID： PMC5413200 DOI： 10.1109/EMBC.2015.7318762
Source DB: PubMed Journal: Conf Proc IEEE Eng Med Biol Soc ISSN： 1557-170X