Mutual Information Analysis of Brain-Body Interactions during different Levels of Mental stress

Luca Faes, Riccardo Pernice, Alessandro Busacca, Mariolin De Cecco, Matteo Zanetti, Mariolin De Cecco, Giandomenico Nollo

Risultato della ricerca: Conference contribution

5 Citazioni (Scopus)

Abstract

In this work, we analyze brain-heart interactions during different mental states computing mutual information (MI) between the dynamic activity of different physiological systems. In 18 healthy subjects monitored in a relaxed resting state and during a mental arithmetic and a serious game task, multichannel EEG, one lead ECG, respiration and blood volume pulse were collected via wireless non-invasive biosensors. From these signals, synchronous 300-second time series were extracted measuring brain activity via the δ, θ, α, and β EEG power, and activity of the body district via the ECG R-R interval η, the respiratory amplitude ϱ and the pulse arrival time π. MI was computed using a linear estimator: (i) between {η,ϱ,π} and {δ,θ,α,β}, to measure overall brain-body interactions; (ii) between each time series and the others of the same district, to measure information shared within a district; and (iii) between each time series of a district and all series of the other district, to evaluate individual contributions to the information shared between brain and body. Results document the existence of statistically significant brain-body interactions, with high MI values involving mainly the η body dynamics and the δ and β brain dynamics. State-dependent variations were mostly relevant to the MI of the brain system involving δ, θ, α during mental arithmetic, and α and β during serious game. Thus, MI can be useful to detect correlated activity within and between brain and body systems monitored simultaneously during different mental states.
Lingua originaleEnglish
Titolo della pubblicazione ospite2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Pagine6176-6179
Numero di pagine4
Stato di pubblicazionePublished - 2019

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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