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PLoS One. 2014 Feb 3;9(2):e87798. doi: 10.1371/journal.pone.0087798. eCollection 2014.

Correlations between the signal complexity of cerebral and cardiac electrical activity: a multiscale entropy analysis.

Author information

  • 1Department of Geriatrics, Tainan Hospital, Ministry of Health and Welfare, Tainan, Taiwan ; Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan.
  • 2Research Center for Adaptive Data Analysis, National Central University, Jongli, Taiwan.
  • 3Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan.
  • 4Graduate Institute of Communication Engineering, National Taiwan University, Taipei, Taiwan.
  • 5Research Center for Adaptive Data Analysis, National Central University, Jongli, Taiwan ; Institute of Systems Biology and Bioinformatics, National Central University, Jongli, Taiwan.
  • 6Graduate Institute of Clinical Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan ; Cardiology, Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan.

Abstract

The heart begins to beat before the brain is formed. Whether conventional hierarchical central commands sent by the brain to the heart alone explain all the interplay between these two organs should be reconsidered. Here, we demonstrate correlations between the signal complexity of brain and cardiac activity. Eighty-seven geriatric outpatients with healthy hearts and varied cognitive abilities each provided a 24-hour electrocardiography (ECG) and a 19-channel eye-closed routine electroencephalography (EEG). Multiscale entropy (MSE) analysis was applied to three epochs (resting-awake state, photic stimulation of fast frequencies (fast-PS), and photic stimulation of slow frequencies (slow-PS)) of EEG in the 1-58 Hz frequency range, and three RR interval (RRI) time series (awake-state, sleep and that concomitant with the EEG) for each subject. The low-to-high frequency power (LF/HF) ratio of RRI was calculated to represent sympatho-vagal balance. With statistics after Bonferroni corrections, we found that: (a) the summed MSE value on coarse scales of the awake RRI (scales 11-20, RRI-MSE-coarse) were inversely correlated with the summed MSE value on coarse scales of the resting-awake EEG (scales 6-20, EEG-MSE-coarse) at Fp2, C4, T6 and T4; (b) the awake RRI-MSE-coarse was inversely correlated with the fast-PS EEG-MSE-coarse at O1, O2 and C4; (c) the sleep RRI-MSE-coarse was inversely correlated with the slow-PS EEG-MSE-coarse at Fp2; (d) the RRI-MSE-coarse and LF/HF ratio of the awake RRI were correlated positively to each other; (e) the EEG-MSE-coarse at F8 was proportional to the cognitive test score; (f) the results conform to the cholinergic hypothesis which states that cognitive impairment causes reduction in vagal cardiac modulation; (g) fast-PS significantly lowered the EEG-MSE-coarse globally. Whether these heart-brain correlations could be fully explained by the central autonomic network is unknown and needs further exploration.

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