A Compressed Sensing Based Decomposition of Electrodermal Activity Signals

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

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

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.

Original languageEnglish (US)
Article number7755834
Pages (from-to)2142-2151
Number of pages10
JournalIEEE Transactions on Biomedical Engineering
Volume64
Issue number9
DOIs
StatePublished - Sep 2017

Keywords

  • Compressed sensing
  • electrodermal activity (EDA)
  • galvanic skin response
  • sparse deconvolution
  • wearables

Fingerprint Dive into the research topics of 'A Compressed Sensing Based Decomposition of Electrodermal Activity Signals'. Together they form a unique fingerprint.

Cite this