Causal Relationship Analysis of Heart Rate Variability and Power Spectral Density Time Series of Electroencephalographic Signals

Marinieves Pardo-rodriguez and Erik Bojorges-Valdez
Universidad Iberoamericana


Aims: This study aimed to find whether there is G-causality between the power spectral density time series (PSDts) of alpha, beta and gamma brainwaves and components of the heart rate variability (HRV), in order to determine the connectivity underlying the autonomic nervous system. Methods: 21 EEG channels and one ECG derivation of 14 subjects were recorded during idle state and a controlled breathing task. The EEG signals were separated into alpha, beta and gamma frequency ranges and their PSDts were estimated. The RR intervals from the ECG were used to obtain the HRV signal, which was separated into intrinsic mode functions (IMFs) using the empirical mode decomposition method. Granger causality tests were run for the PSDts of the brainwaves described and the HRV signals IMFs. Results: A G-causal relation was found between the PSDts of alpha, beta and gamma waves and the HRV IMFs. G-causality increased for three different conditions: slower IMFs (IMF4), PSDts of higher frequency (gamma) band and during task realization. Meaning, gamma’s PSDts G-caused HRV for a larger number of subjects and channels. Also there was a larger incidence on the number of channels that G-caused HRV during the breathing control task. Conclusion: There is a causal influence from the PSDts of EEG signals to the HRV IMFs, leading to believe there is an indirect or unobserved interaction between instantaneous changes on EEG power spectral density and components of HRV. Knowing this interaction might be useful for a better understanding of HRV analysis in different applications, since it may help explain or predict changes in its behavior.