Granger causality suggests an association between heart rate variability and EEG band power dynamics

Victoria De la Cruz-Armienta; Erik Bojorges-Valdez; Oscar Yanez-Suarez

doi:10.1117/12.2256938

26 January 2017 Granger causality suggests an association between heart rate variability and EEG band power dynamics

Victoria De la Cruz-Armienta, Erik Bojorges-Valdez, Oscar Yanez-Suarez

Author Affiliations +

Proceedings Volume 10160, 12th International Symposium on Medical Information Processing and Analysis; 1016005 (2017) https://doi.org/10.1117/12.2256938
Event: 12th International Symposium on Medical Information Processing and Analysis, 2016, Tandil, Argentina

Abstract

To search for possible interactions between the autonomic heart control and the brain activity we have evaluated the relation between heart rate variability (HRV) and the alpha and beta EEG band power dynamics. The experiments consisted in the alteration of HRV induced by respiratory rhythm changes. Modifications in the time series of the spectral density in each band {causally related to the HRV{ were recognized in the central and occipital regions using a Granger causality approach. The number of subjects and distribution of channels in which there was a causal relation changed with the different tasks assigned to the subjects and the analyzed bands. The causal relation between HRV and beta power series was observed in at least one channel, in eight out of nine subjects for a resting condition. On the other hand, in the HRV and alpha power series analysis the change in distribution was less pronounced, and detected in a smaller number of subjects. The results suggest that the HRV may be associated with the spectral band power time series dynamics, and this relationship is altered by the respiration.

Citation Download Citation

Victoria De la Cruz-Armienta, Erik Bojorges-Valdez, and Oscar Yanez-Suarez "Granger causality suggests an association between heart rate variability and EEG band power dynamics", Proc. SPIE 10160, 12th International Symposium on Medical Information Processing and Analysis, 1016005 (26 January 2017); https://doi.org/10.1117/12.2256938

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