The human body can be seen as a functional network depicting the dynamical interactions between different organ systems. This exchange of information is often evaluated with information-theoretic approaches which comprise the use of vector autoregressive (VAR) and state space (SS) models, normally identified with the Ordinary Least Squares (OLS). However, the number of time series to be included in the model is strictly related to the length of data recorded thus limiting the use of the classical approach. In this work, a new method based on penalized regressions, the so-called LASSO, was compared with OLS on physiological time-series extracted from 18 subjects during different stress conditions. Results show similarities between the brain-body interactions estimated by both methodologies, highlighting a greater intepretability of patterns estimated with LASSO especially in the subnetwork of brain-brain interactions.

Antonacci, Y., Astolfi, L., Busacca, A., Pernice, R., Nollo, G., & Faes, L. (2020). Model-Based Transfer Entropy Analysis of Brain-Body Interactions with Penalized regression techniques. In 2020 11th Conference of the European Study Group on Cardiovascular Oscillations (ESGCO) (pp. 1-2) [10.1109/ESGCO49734.2020.9158165].

Model-Based Transfer Entropy Analysis of Brain-Body Interactions with Penalized regression techniques

Antonacci, Yuri
;
Busacca, Alessandro;Pernice, Riccardo;Faes, Luca
2020

Abstract

The human body can be seen as a functional network depicting the dynamical interactions between different organ systems. This exchange of information is often evaluated with information-theoretic approaches which comprise the use of vector autoregressive (VAR) and state space (SS) models, normally identified with the Ordinary Least Squares (OLS). However, the number of time series to be included in the model is strictly related to the length of data recorded thus limiting the use of the classical approach. In this work, a new method based on penalized regressions, the so-called LASSO, was compared with OLS on physiological time-series extracted from 18 subjects during different stress conditions. Results show similarities between the brain-body interactions estimated by both methodologies, highlighting a greater intepretability of patterns estimated with LASSO especially in the subnetwork of brain-brain interactions.
Settore ING-INF/06 - Bioingegneria Elettronica E Informatica
978-1-7281-5751-1
Antonacci, Y., Astolfi, L., Busacca, A., Pernice, R., Nollo, G., & Faes, L. (2020). Model-Based Transfer Entropy Analysis of Brain-Body Interactions with Penalized regression techniques. In 2020 11th Conference of the European Study Group on Cardiovascular Oscillations (ESGCO) (pp. 1-2) [10.1109/ESGCO49734.2020.9158165].
File in questo prodotto:
File Dimensione Formato  
2020_Antonacci_ESGCO2020_published.pdf

non disponibili

Descrizione: Articolo pubblicato
Tipologia: Versione Editoriale
Dimensione 337.19 kB
Formato Adobe PDF
337.19 kB Adobe PDF   Visualizza/Apri   Richiedi una copia
2020_Antonacci_ESGCO2020_postprint.pdf

non disponibili

Descrizione: Post-print
Tipologia: Post-print
Dimensione 298.49 kB
Formato Adobe PDF
298.49 kB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10447/430388
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 1
  • ???jsp.display-item.citation.isi??? 1
social impact