cvxEDA: A Convex Optimization Approach to Electrodermal Activity Processing.
OBJECTIVE
This paper reports on a novel algorithm for the analysis of electrodermal activity (EDA) using methods of convex optimization. EDA can be considered as one of the most common observation channels of sympathetic nervous system activity, and manifests itself as a change in electrical properties of the skin, such as skin conductance (SC).


METHODS
The proposed model describes SC as the sum of three terms: the phasic component, the tonic component, and an additive white Gaussian noise term incorporating model prediction errors as well as measurement errors and artifacts. This model is physiologically inspired and fully explains EDA through a rigorous methodology based on Bayesian statistics, mathematical convex optimization, and sparsity.


RESULTS
The algorithm was evaluated in three different experimental sessions to test its robustness to noise, its ability to separate and identify stimulus inputs, and its capability of properly describing the activity of the autonomic nervous system in response to strong affective stimulation.


CONCLUSIONS
Results are very encouraging, showing good performance of the proposed method and suggesting promising future applicability, e.g., in the field of affective computing.
Alberto
A
Greco
Gaetano
G
Valenza
Antonio
A
Lanata
Enzo Pasquale
EP
Scilingo
Luca
L
Citi
D000293:Adolescent; D000328:Adult; D000465:Algorithms; D001499:Bayes Theorem; D004594:Electrophysiology / Q000379:methods*; D005260:Female; D005712:Galvanic Skin Response / Q000502:physiology*; D006801:Humans; D008297:Male; D016011:Normal Distribution; D012815:Signal Processing, Computer-Assisted; D055815:Young Adult