Work

Wavelet-based motion artifact removal for electrodermal activity

Year: 2015

Type: conference-paper

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.

Source: Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

DOI: 10.1109/embc.2015.7319814

Authors: Weixuan Chen, Natasha Jaques, Sara Taylor, Akane Sano, Szymon Fedor, Rosalind W. Picard

Institution: Massachusetts Institute of Technology

Language: English

Topic: Emotion and Mood Recognition

Subfield: Experimental and Cognitive Psychology

Field: Psychology

Domain: Social Sciences

Open Access status: green

Funder: National Institute of General Medical Sciences

Award: Multi-scale modeling of sleep behaviors in social networks