Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

2D NMR: Overview of Heteronuclear Correlation Techniques01:18

2D NMR: Overview of Heteronuclear Correlation Techniques

Heteronuclear correlation spectroscopy is an analytical technique that investigates the coupling between different types of nuclei, often a proton and an X-nucleus, such as carbon-13 or nitrogen-15. This method is commonly used in nuclear magnetic resonance (NMR) spectroscopy to gain insights into complex chemical compounds' structural and compositional aspects. A typical heteronuclear correlation spectrum displays X-nucleus chemical shifts on one axis and a proton spectrum on the other axis.
Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

The electrical signals recorded on an electrocardiogram (ECG) occur before the mechanical processes of contraction and relaxation during the cardiac cycle.
A cardiac action potential originates in the SA node and spreads throughout the atria and the AV node in approximately 0.03 seconds. This results in the P wave in an ECG and triggers atrial contraction. The action potential is then briefly slowed at the AV node, allowing the atria to contract and fill the ventricles with blood before...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Widespread Occurrence of Tire Wear <i>p</i>-Phenylenediamines and Their Quinones in Cloud Water.

Environmental science & technology·2026
Same author

Enrichment in More Soluble Ferric Iron (Fe(III)<sub>S</sub>) during Dust Transport by Nitric Acid Uptake.

Environmental science & technology·2026
Same author

OVOCs drive radical cycling and ozone formation in background air.

Environmental science and ecotechnology·2026
Same author

Clinical Efficacy and Safety Assessment of Specific-Mode Electroacupuncture Stimulation Combined With Paclitaxel for Recurrent Malignant Gliomas: Study Protocol for a Single-Arm Trial.

JMIR research protocols·2026
Same author

Sex-dependent locus coeruleus vulnerability in Alzheimer's disease: gut dysbiosis as a driver and probiotic intervention as rescue.

Biology of sex differences·2026
Same author

Upper-layer ozone intrusion promotes wintertime secondary aerosol formation on the ground.

National science review·2026
Same journal

Does early gastrostomy tube placement after stroke improve functional recovery and quality of life? A literature-informed pathway-decomposition analysis.

Neurological research·2026
Same journal

Predictive ability of cardiac biomarkers for early risk stratification and 3-month functional outcomes after reperfusion therapy in acute ischemic stroke.

Neurological research·2026
Same journal

Luteolin reduces sciatic nerve damage and modulates TRPV1 and TRPM2 expression in diabetic rats.

Neurological research·2026
Same journal

Cholinergic regulation of memory retrieval: scopolamine reduces hippocampal neurotrophic and metabolic support.

Neurological research·2026
Same journal

Effect of intravenous thrombolysis on arterial and venous profiles in large-vessel occlusion stroke: a retrospective propensity score-matched study.

Neurological research·2026
Same journal

Association between post-treatment neuroimaging markers of cerebral small vessel disease and short-term efficacy of recombinant tissue plasminogen activator in acute stroke patients.

Neurological research·2026
See all related articles

Related Experiment Video

Updated: May 27, 2026

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

EEG non-linear feature extraction using correlation dimension and Hurst exponent.

Shujuan Geng1, Weidong Zhou, Qi Yuan

  • 1School of Information Science and Engineering, Shandong University, Jinan, China.

Neurological Research
|November 15, 2011
PubMed
Summary
This summary is machine-generated.

Non-linear analysis reveals distinct complexity in epileptic and interictal electroencephalogram (EEG) signals. Epileptic EEG exhibits lower correlation dimension and approximate entropy, indicating reduced complexity and increased anticorrelation compared to interictal EEG.

More Related Videos

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
11:15

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

Related Experiment Videos

Last Updated: May 27, 2026

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
11:15

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Epilepsy diagnosis relies on electroencephalogram (EEG) analysis.
  • Distinguishing between epileptic and interictal (seizure-free) EEG states is crucial for patient management.
  • Traditional EEG analysis may not fully capture the complex dynamics of brain activity during seizures.

Purpose of the Study:

  • To investigate the differences in complexity and dynamics between epileptic and interictal EEG signals.
  • To evaluate the utility of non-linear parameters in characterizing EEG during seizures.
  • To explore the neurodynamic changes associated with epileptic activity.

Main Methods:

  • Calculation of non-linear features: Correlation Dimension (CD) and Hurst exponent (H).
  • Analysis of 100 segments each of epileptic EEG and interictal EEG.
  • Computation of Approximate Entropy (ApEn) for both EEG types.

Main Results:

  • Mean CD values: 2.64 (epileptic EEG) vs. 4.55 (interictal EEG).
  • Mean ApEn values: 0.90 (epileptic EEG) vs. 4.55 (interictal EEG).
  • Mean Hurst exponent values: 0.19 (epileptic EEG) vs. 0.29 (interictal EEG).
  • Epileptic EEG showed lower CD and ApEn, indicating reduced complexity.
  • Both EEG types exhibited long-range anticorrelation (Hurst exponent <0.5), with epileptic EEG showing stronger anticorrelation.

Conclusions:

  • Non-linear parameters like CD and Hurst exponent effectively differentiate between epileptic and interictal EEG.
  • Reduced complexity and increased anticorrelation characterize epileptic EEG signals.
  • These non-linear measures offer valuable insights into the neurodynamics of epilepsy.