A study of heart–brain information flow across sleep stages using transfer entropy

Heart–brain coupling, regulated by the autonomic and central nervous systems, plays a vital role in maintaining physiological homeostasis, emotional regulation, and cognitive function. Understanding this dynamic interaction offers valuable insights into the coordination of physiological processes and the diagnosis of cardiovascular and neurological disorders. In this study, we applied symbolic phase transfer entropy to analyze directional information flow between the heart and brain during different sleep stages. The results revealed that heart-to-brain information flow consistently dominates across all frequency bands and sleep stages, with stronger coupling in lower-frequency bands during sleep and diminished coupling in higher-frequency bands associated with wakefulness. Significant differences in transfer entropy were observed between wakefulness and sleep, particularly in the δ and β bands, with consistent trends across electrodes C3, C4, O1, and O2. Using the identified transfer entropy differences as features, a K-nearest neighbors classifier achieved a mean accuracy of 85.43% and an AUC of 0.9276, demonstrating robust performance in distinguishing wakefulness from sleep states. These findings highlight the innovative application of directional information flow metrics in understanding heart–brain interactions and their potential for advancing sleep state diagnostics.