Information-Theoretic Analysis of EEG Wave Amplitude and Heart Rate Variability Reveals the Time Scale-Dependent Nature of Brain–Heart Interactions

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Goal:</i> Brain-heart interactions have been linked to physiological and pathological states and are typically studied through the use of electroencephalographic (EEG) signal and heart rate variability (HRV) time series. However, there are still major challenges to overcome, particularly in establishing a robust methodology to assess these complex multi-scale interactions and to extract meaningful information. To this end, we explore the time scale-dependent nature of brain-heart interactions by exploiting information-theoretic measures. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Methods:</i> We analyze cardiac vagal activity and EEG brain wave amplitudes at two time scales—heart rhythm (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula>1 s) and longer (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula>1 min)—in two groups of healthy subjects monitored during wakefulness and sleep, respectively. Different entropy-based measures are then employed to evaluate the regularity of each system's dynamics, as well as their static and dynamic coupling. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Results:</i> Different time-scales are involved in different physiological coupling mechanisms. While overall coupling strength values are low, longer time-scales show a stronger presence of coupling in terms of statistically validated brain-heart connections compared to shorter time-scales. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Conclusions:</i> This study shows that the presence and the strength of brain-heart interactions are highly dependent on the time-scale, which in turn is affected by the underlying physiological processes.

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Journal
IEEE Open Journal of Engineering in Medicine and Biology
Published
2025-01-01
DOI
https://doi.org/10.1109/ojemb.2025.3590598
Primary Topic
EEG and Brain-Computer Interfaces
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article
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article

Information-Theoretic Analysis of EEG Wave Amplitude and Heart Rate Variability Reveals the Time Scale-Dependent Nature of Brain–Heart Interactions

Yuri Antonacci, Fabrice Jurysta, Francesca Ferri, Luca Faes et al.
1 links
IEEE Open Journal of Engineering in Medicine and Biology
EEG and Brain-Computer Interfaces
article

Information-Theoretic Analysis of EEG Wave Amplitude and Heart Rate Variability Reveals the Time Scale-Dependent Nature of Brain–Heart Interactions

Yuri Antonacci, Fabrice Jurysta, Francesca Ferri, Luca Faes, Andrea Zaccaro, Chiara Barà, Valeria R. Vergara
article en

Abstract

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Goal:</i> Brain-heart interactions have been linked to physiological and pathological states and are typically studied through the use of electroencephalographic (EEG) signal and heart rate variability (HRV) time series. However, there are still major challenges to overcome, particularly in establishing a robust methodology to assess these complex multi-scale interactions and to extract meaningful information. To this end, we explore the time scale-dependent nature of brain-heart interactions by exploiting information-theoretic measures. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Methods:</i> We analyze cardiac vagal activity and EEG brain wave amplitudes at two time scales—heart rhythm (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula>1 s) and longer (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula>1 min)—in two groups of healthy subjects monitored during wakefulness and sleep, respectively. Different entropy-based measures are then employed to evaluate the regularity of each system's dynamics, as well as their static and dynamic coupling. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Results:</i> Different time-scales are involved in different physiological coupling mechanisms. While overall coupling strength values are low, longer time-scales show a stronger presence of coupling in terms of statistically validated brain-heart connections compared to shorter time-scales. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Conclusions:</i> This study shows that the presence and the strength of brain-heart interactions are highly dependent on the time-scale, which in turn is affected by the underlying physiological processes.

IEEE Open Journal of Engineering in Medicine and BiologyVol. 6
University of Chieti-Pescara (IT), 21st Century Oncology (United States) (US), University of Palermo (IT)
Openalex Percentile: Top 81%
EEG and Brain-Computer Interfaces
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