Amplitude-aware symbolic transfer entropy for heart–brain coupling analysis across sleep stages
Source abstract
Abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: EEG and Brain-Computer Interfaces · Neural dynamics and brain function · Heart Rate Variability and Autonomic Control
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Tongda College of Nanjing University of Posts and Telecommunications 1 , Yangzhou 225127,
Office of Financial Affairs, Nanjing University of Posts and Telecommunications 2 , Nanjing 210023,
Smart Health Big Data Analysis and Location Services Engineering Research Center of Jiangsu Province, Nanjing University of Posts and Telecommunications 3 , Nanjing 210023,
Funding
Tongda College of Nanjing University of Posts and Telecommunications
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