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Correlation between ECG and Cardiac Cycle

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Related Experiment Video

Updated: May 8, 2026

Quantifying Infra-slow Dynamics of Spectral Power and Heart Rate in Sleeping Mice
10:56

Quantifying Infra-slow Dynamics of Spectral Power and Heart Rate in Sleeping Mice

Published on: August 2, 2017

Investigating the interaction between heart rate variability and sleep EEG using nonlinear algorithms.

Jia-Rong Yeh1, Chung-Kang Peng, Men-Tzung Lo

  • 1Research Center for Adaptive Data Analysis and Center for Dynamical Biomarkers and Translational Medicine, National Central University, Taoyuan, Taiwan.

Journal of Neuroscience Methods
|August 23, 2013
PubMed
Summary

Sleep EEG and heart rate variability (HRV) dynamics reveal sleep depth and REM sleep stages. Novel analysis shows EEG fast-wave amplitude correlates with HRV, outperforming traditional methods.

Keywords:
Detrended fluctuation analysisHeart rate variabilityHilbert Huang transformSleep EEGSympathovagal modulation

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Related Experiment Videos

Last Updated: May 8, 2026

Quantifying Infra-slow Dynamics of Spectral Power and Heart Rate in Sleeping Mice
10:56

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Published on: August 2, 2017

BrainBeats as an Open-Source EEGLAB Plugin to Jointly Analyze EEG and Cardiovascular Signals
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BrainBeats as an Open-Source EEGLAB Plugin to Jointly Analyze EEG and Cardiovascular Signals

Published on: April 26, 2024

Area of Science:

  • Neuroscience
  • Physiology
  • Biomedical Engineering

Background:

  • Sleep electroencephalogram (EEG) exhibits multi-mode modulation, crucial for understanding sleep stages.
  • Heart rate variability (HRV) fractal properties reflect autonomic nervous system (ANS) activity.
  • Both EEG and HRV are clinically relevant for sleep status assessment.

Purpose of the Study:

  • To characterize temporal features of sleep EEG oscillations using Hilbert Huang Transform (HHT).
  • To assess short-term HRV properties using Detrended Fluctuation Analysis (DFA).
  • To examine the dynamic interaction between sleep EEG and HRV during sleep.

Main Methods:

  • Polysomnographic recordings from 19 healthy female subjects.
  • Hilbert Huang Transform (HHT) with masking signals for EEG analysis.
  • Detrended Fluctuation Analysis (DFA) to calculate DFA α1 for HRV analysis.

Main Results:

  • Sleep EEG frequency features indicate non-rapid eye movement (NREM) sleep depth.
  • Sleep EEG fast-wave oscillation amplitude distinguishes rapid eye movement (REM) from NREM sleep.
  • A significant correlation was found between DFA α1 of HRV and mean amplitude of fast-wave EEG oscillations.

Conclusions:

  • The dynamic properties of sleep EEG and HRV, analyzed via Empirical Mode Decomposition (EMD) and DFA, offer valuable insights into cortical and ANS activity during sleep.
  • Correlation between HRV's DFA α1 and EEG fast-wave amplitude is more significant than traditional spectral analysis methods.