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 Experiment Videos

Large-scale dimension densities for heart rate variability analysis.

Corinna Raab1, Niels Wessel, Alexander Schirdewan

  • 1Center for Dynamics of Complex Systems, Institute of Physics, University of Potsdam, Potsdam, Germany. corinna@agnld.uni-potsdam.de

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|May 23, 2006
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Winners of 2022 Edward Norton Lorenz Early Career Awards.

Chaos (Woodbury, N.Y.)·2023
Same author

The ocean carbon sinks and climate change.

Chaos (Woodbury, N.Y.)·2023
Same author

Arctic weather variability and connectivity.

Nature communications·2023
Same author

Mechanisms of Photostimulation of Brain's Waste Disposal System: The Role of Singlet Oxygen.

Advances in experimental medicine and biology·2023
Same author

Diffusion Source Inference for Large-Scale Complex Networks Based on Network Percolation.

IEEE transactions on neural networks and learning systems·2023
Same author

Adaptive resonance and control of chaos in a new memristive generalized FitzHugh-Nagumo bursting model.

Chaos (Woodbury, N.Y.)·2023

This study introduces large-scale dimension densities (LASDID) to analyze heart rate variability (HRV). The method effectively distinguishes cardiac disease patients from healthy individuals and computer-generated data.

Area of Science:

  • Cardiology
  • Nonlinear Dynamics
  • Biomedical Signal Processing

Background:

  • Heart rate variability (HRV) analysis is crucial for understanding cardiac health.
  • Traditional HRV methods face challenges with short, nonstationary, and unfiltered data.
  • Existing techniques may struggle to differentiate between real physiological signals and artifacts.

Purpose of the Study:

  • To reanalyze 2002 Computers in Cardiology (CiC) Challenge data using large-scale dimension densities (LASDID).
  • To apply LASDID to differentiate between healthy individuals and patients with cardiac diseases (atrial fibrillation, congestive heart failure).
  • To investigate the potential of LASDID for clinical risk stratification and distinguishing real from simulated data.

Main Methods:

  • Estimation of large-scale dimension density (LASDID) from time series using a normalized Grassberger-Procaccia algorithm.

Related Experiment Videos

  • Correction of systematic errors caused by boundary effects in large-scale analysis.
  • Application to short, nonstationary, and unfiltered HRV data, including day/night comparisons.
  • Analysis of data from healthy young (YH), elderly (EH), atrial fibrillation (AF), and congestive heart failure (CHF) patients.

    • Main Results:

    • LASDID successfully distinguished computer-generated data from real HRV data from the CiC 2002 challenge.
    • The AF group was completely separated from other groups (rho = 0.97 +/- 0.02).
    • CHF patients showed significant differences from healthy controls, particularly during daytime (CHF: 0.65 +/- 0.13; EH: 0.54 +/- 0.05; YH: 0.57 +/- 0.05).
    • CHF patients lacked circadian changes in LASDID, unlike healthy controls (p=0.002).

    Conclusions:

    • LASDID is a robust method for analyzing complex time series like HRV, even with short, nonstationary data.
    • The technique demonstrates high potential for differentiating cardiac disease states and identifying simulated data.
    • LASDID offers a potentially independent approach for clinical risk stratification in cardiovascular diseases.