title: A Compressed Sensing Based Decomposition of Electrodermal Activity Signals.
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.
authors.forename: Swayambhoo
authors.initials: S
authors.lastname: Jain
authors.forename: Urvashi
authors.initials: U
authors.lastname: Oswal
authors.forename: Kevin Shuai
authors.initials: KS
authors.lastname: Xu
authors.forename: Brian
authors.initials: B
authors.lastname: Eriksson
authors.forename: Jarvis
authors.initials: J
authors.lastname: Haupt
mesh_terms: D000328:Adult; D000465:Algorithms; D044962:Data Compression / Q000379:methods*; D005260:Female; D005712:Galvanic Skin Response / Q000502:physiology*; D006801:Humans; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D059629:Signal-To-Noise Ratio