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.

Model-Based Transfer Entropy Analysis of Brain-Body Interactions with Penalized regression techniques / Antonacci, Y.; Astolfi, L.; Busacca, A.; Pernice, R.; Nollo, G.; Faes, L.. - (2020), pp. 1-2. ((Intervento presentato al convegno 11th Conference of the European Study Group on Cardiovascular Oscillations, ESGCO 2020 tenutosi a Pisa, I nel July, 2020 [10.1109/ESGCO49734.2020.9158165].

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

Nollo G.;
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.
2020 11th Conference of the European Study Group on Cardiovascular Oscillations: Computation and Modelling in Physiology: New Challenges and Opportunities, ESGCO 2020
Italia
Institute of Electrical and Electronics Engineers Inc.
978-1-7281-5751-1
Antonacci, Y.; Astolfi, L.; Busacca, A.; Pernice, R.; Nollo, G.; Faes, L.
Model-Based Transfer Entropy Analysis of Brain-Body Interactions with Penalized regression techniques / Antonacci, Y.; Astolfi, L.; Busacca, A.; Pernice, R.; Nollo, G.; Faes, L.. - (2020), pp. 1-2. ((Intervento presentato al convegno 11th Conference of the European Study Group on Cardiovascular Oscillations, ESGCO 2020 tenutosi a Pisa, I nel July, 2020 [10.1109/ESGCO49734.2020.9158165].
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11572/295490
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