Wavelet-Based Motion Artifact Removal for Electrodermal Activity

Chen, W., Jaques, N., Taylor, S., Sano, A., Fedor, S., and Picard, R. "Wavelet-Based Motion Artifact Removal for Electrodermal Activity" In Proc. International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milan, Italy, August 2015.


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.

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