Abstract
Supplemental information captured from HRV can provide deeper insight into nervous system function and consequently improve evaluation of brain function. Therefore, it is of interest to combine both EEG and HRV. However, irregular nature of time spans between adjacent heartbeats makes the HRV hard to be directly fused with EEG timeseries. Current study performed a pioneering work in integrating EEG-HRV information in a single marker called cumulant ratio, quantifying how far EEG dynamics deviate from self-similarity compared to HRV dynamics. Experimental data recorded using BrainStatus device with single ECG and 10 EEG channels from healthy-brain patients undergoing operation (N = 20) were used for the validation of the proposed method. Our analyses show that the EEG to HRV ratio of first, second and third cumulants gets systematically closer to zero with increase in depth of anesthesia, respectively 29.09%, 65.0% and 98.41%. Furthermore, extracting multifractality properties of both heart and brain activities and encoding them into a 3-sample numeric code of relative cumulants does not only encapsulates the comparison of two evenly and unevenly spaced variables of EEG and HRV into a concise unitless quantity, but also reduces the impact of outlying data points.
Highlights
Heart rate variability (HRV) is referred to variation of the time span between consecutive heartbeats over time [1]
According to the figure 4, HRV reduced during anesthesia
While both LF and high frequency (HF) components were lower during anesthesia, increased very low frequency component was observed during anesthesia
Summary
Heart rate variability (HRV) is referred to variation of the time span between consecutive heartbeats over time [1]. This beat-to-beat variation reflect the oscillating nature of regulation mechanism within autonomic nervous system (ANS). HRV and Electroencephalograph (EEG) can be highly correlated, and their joint information can give important insights into heart-brain communication [2]. Many studies have attempted to provided evidence indicating a correlation between EEG and HRV. The existence of directional causal relationship between combination of intrinsic mode functions of HRV and band powers of EEG was verified using Granger causality test [7]. Correlations between the EEG delta power and normalized HF power of HRV as well as correlation between the fractal property of HRV and EEG fast-wave oscillation were verified [8]
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call