Wavelet-based motion artifact removal for electrodermal activity

• Main Authors: Chen, Weixuan (Contributor), Jaques, Natasha Mary (Contributor), Taylor, Sara Ann (Contributor), Sano, Akane (Contributor), Fedor, Szymon (Contributor), Picard, Rosalind W. (Contributor)
• Other Authors: Massachusetts Institute of Technology. Media Laboratory (Contributor), Program in Media Arts and Sciences (Massachusetts Institute of Technology) (Contributor)
• Format: Article
• Language: English
• Published: Institute of Electrical and Electronics Engineers (IEEE), 2017-07-12T16:59:36Z.
• Subjects: Article
• Online Access: Get fulltext

 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.