Algorithm for the analysis of electrodermal activity (EDA) using convex optimization
Al momento, stai seguendo questo contributo
- Vedrai gli aggiornamenti nel tuo feed del contenuto seguito
- Potresti ricevere delle email a seconda delle tue preferenze per le comunicazioni
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 (2026). cvxEDA (https://github.com/lciti/cvxEDA), GitHub. Recuperato .
Informazioni generali
Compatibilità della release di MATLAB
- Compatibile con qualsiasi release
Compatibilità della piattaforma
- Windows
- macOS
- Linux
Le versioni che utilizzano il ramo predefinito di GitHub non possono essere scaricate
| Versione | Pubblicato | Note della release | Action |
|---|---|---|---|
| 1.0.0.0 |
