| Literature DB >> 26737714 |
Weixuan Chen, Natasha Jaques, Sara Taylor, Akane Sano, Szymon Fedor, Rosalind W Picard.
Abstract
Electrodermal activity (EDA) recording is a powerful, widely used tool for monitoring psychological or physiological arousal. However, analysis of EDA is hampered by its sensitivity to motion artifacts. We propose a method for removing motion artifacts from EDA, measured as skin conductance (SC), using a stationary wavelet transform (SWT). We modeled the wavelet coefficients as a Gaussian mixture distribution corresponding to the underlying skin conductance level (SCL) and skin conductance responses (SCRs). The goodness-of-fit of the model was validated on ambulatory SC data. We evaluated the proposed method in comparison with three previous approaches. Our method achieved a greater reduction of artifacts while retaining motion-artifact-free data.Entities:
Mesh：
Year: 2015 PMID： 26737714 PMCID： PMC5413204 DOI： 10.1109/EMBC.2015.7319814
Source DB: PubMed Journal: Conf Proc IEEE Eng Med Biol Soc ISSN： 1557-170X