A Compressed Sensing Based Decomposition of Electrodermal Activity Signals.

Citation data:

IEEE transactions on bio-medical engineering, ISSN: 1558-2531, Vol: 64, Issue: 9, Page: 2142-2151

Publication Year:
2017
Usage 63
Abstract Views 63
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Readers 6
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PMID:
27893381
DOI:
10.1109/tbme.2016.2632523
Author(s):
Jain, Swayambhoo, Oswal, Urvashi, Xu, Kevin Shuai, Eriksson, Brian, Haupt, Jarvis
Publisher(s):
Institute of Electrical and Electronics Engineers (IEEE)
Tags:
Engineering
article description
The measurement and analysis of electrodermal activity (EDA) offers applications in diverse areas ranging from market research to seizure detection and to human stress analysis. Unfortunately, the analysis of EDA signals is made difficult by the superposition of numerous components that can obscure the signal information related to a user's response to a stimulus. We show how simple preprocessing followed by a novel compressed sensing based decomposition can mitigate the effects of the undesired noise components and help reveal the underlying physiological signal. The proposed framework allows for decomposition of EDA signals with provable bounds on the recovery of user responses. We test our procedure on both synthetic and real-world EDA signals from wearable sensors and demonstrate that our approach allows for more accurate recovery of user responses as compared with the existing techniques.

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