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.

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.