Updated 21 Nov 2022
This program implements the cvxEDA algorithm for the analysis of electrodermal activity (EDA) using methods of convex optimization, described in:
A Greco, G Valenza, A Lanata, EP Scilingo, and L Citi
"cvxEDA: a Convex Optimization Approach to Electrodermal Activity Processing"
IEEE Transactions on Biomedical Engineering, 2015
It is based on a model which describes EDA 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.
Luca Citi (2023). cvxEDA (https://github.com/lciti/cvxEDA), GitHub. Retrieved .
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