Key result
Transfer entropy from ECG to EEG was consistently higher than from EEG to ECG, with heart-brain information coupling gradually weakening as sleep deepens.
Why the study?
Dynamic interactions between physiological systems like heart-brain coupling are increasingly recognized, but methods to enhance the analysis of EEG and ECG interactions across sleep stages were needed.
The AASTE algorithm provides a novel method for quantifying directional information flow, revealing that heart-to-brain signaling dominates during sleep and weakens with sleep depth.
Answers are grounded in this paper and cite their sources.
May advance sleep-stage coupling quantification; leaves open validation before clinical or outcome use.
Network physiology has increasingly revealed the dynamic interactions between physiological systems, with heart–brain coupling receiving particular attention. In this study, we propose the amplitude-aware symbolic transfer entropy (AASTE) algorithm to enhance the analysis of heart–brain coupling dynamics, specifically focusing on EEG and ECG interactions across different sleep stages: wakefulness (W), light sleep (N1), intermediate sleep (N2), and deep sleep (N3). Our results show that the transfer entropy from ECG to EEG is consistently higher than from EEG to ECG, suggesting a stronger regulatory influence of heart signals on brain activity, particularly during wakefulness. Furthermore, sleep depth significantly impacts heart–brain information coupling, with interactions gradually weakening as sleep deepens, while the central brain regions (C3, C4) play a crucial role in reflecting these changes. This study explores heart–brain interactions across sleep stages and demonstrates AASTE’s potential for quantifying directional information flow in physiological systems, providing a comprehensive tool for investigating the dynamic transmission of physiological information.
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Zhu et al. (2025) studied Sleep stages. Amplitude-aware symbolic transfer entropy (AASTE) algorithm was evaluated on Heart-brain coupling dynamics (transfer entropy). Transfer entropy from ECG to EEG was consistently higher than from EEG to ECG, with heart-brain information coupling gradually weakening as sleep deepens.
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