cvxEDA

Algorithm for the analysis of electrodermal activity (EDA) using convex optimization
1,1K download
Aggiornato 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
DOI: 10.1109/TBME.2015.2474131

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.

Cita come

Luca Citi (2024). cvxEDA (https://github.com/lciti/cvxEDA), GitHub. Recuperato .

Compatibilità della release di MATLAB
Creato con R2015a
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Versione Pubblicato Note della release
1.0.0.0

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