title: Wavelet-based motion artifact removal for electrodermal activity.
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.
authors.forename: Weixuan
authors.initials: W
authors.lastname: Chen
authors.forename: Natasha
authors.initials: N
authors.lastname: Jaques
authors.forename: Sara
authors.initials: S
authors.lastname: Taylor
authors.forename: Akane
authors.initials: A
authors.lastname: Sano
authors.forename: Szymon
authors.initials: S
authors.lastname: Fedor
authors.forename: Rosalind W
authors.initials: RW
authors.lastname: Picard
mesh_terms: D000465:Algorithms; D016477:Artifacts; D005712:Galvanic Skin Response / Q000502:physiology*; D006801:Humans; D009038:Motion; D016011:Normal Distribution; D058067:Wavelet Analysis