A Compressed Sensing Based Decomposition of Electrodermal Activity Signals - PubMed Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2017 Sep;64(9):2142-2151.
doi: 10.1109/TBME.2016.2632523. Epub 2016 Nov 23.

A Compressed Sensing Based Decomposition of Electrodermal Activity Signals

Swayambhoo Jain, Urvashi Oswal, Kevin Shuai Xu, Brian Eriksson, Jarvis Haupt

A Compressed Sensing Based Decomposition of Electrodermal Activity Signals

Swayambhoo Jain et al. IEEE Trans Biomed Eng. 2017 Sep.

Abstract

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.

PubMed Disclaimer

Similar articles

See all similar articles

Cited by

See all "Cited by" articles