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Intrinsic multi-scale analysis: a multi-variate empirical mode decomposition framework.

David Looney1, Apit Hemakom1, Danilo P Mandic1

  • 1Department of Electrical and Electronic Engineering , Imperial College London , London SW7 2AZ, UK.

Proceedings. Mathematical, Physical, and Engineering Sciences
|January 9, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces a novel multi-scale approach using empirical mode decomposition (EMD) to quantify complex system dependencies. The method enhances data association measures and reveals system directionality and coupling in synthetic and biological data.

Keywords:
correlationempirical mode decompositionmulti-variate analysisphase synchronysample entropy

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Area of Science:

  • Complex Systems Analysis
  • Nonlinear Dynamics
  • Signal Processing

Background:

  • Quantifying inter- and intra-component dependence in complex systems is challenging with conventional methods.
  • Existing scale-estimation techniques often fail to capture intrinsic oscillatory behaviors.
  • Accurate analysis of signal dynamics requires decoupling amplitude and phase information.

Approach:

  • Introduced a novel multi-scale framework utilizing empirical mode decomposition (EMD).
  • EMD provides data-driven, intrinsic scales reflecting underlying oscillations.
  • Employed multi-variate EMD extensions for robust scale alignment and feature relevance estimation.

Key Points:

  • Enabled data-driven application of intrinsic correlation, sample entropy (SE), and phase synchrony.
  • Preserved the physical meaning of analysis by operating at intrinsic scale levels.
  • Successfully decoupled amplitude and phase information for accurate correlation and SE analysis.

Conclusions:

  • The multi-scale EMD framework accurately quantifies complex system dynamics.
  • Demonstrated utility in detecting directionality, coupling, and regularity in synthetic and biological systems.
  • Highlights the power of EMD in advancing the analysis of complex system interdependence.