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Real-Time Algorithm for Detrended Cross-Correlation Analysis of Long-Range Coupled Processes.

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  • 1Department of Physiology, Faculty of Medicine, Semmelweis University, Budapest, Hungary.

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Summary
This summary is machine-generated.

This study introduces a fast, real-time algorithm for assessing power-law cross-correlations using detrended cross-correlation analysis (DCCA). The method accurately estimates scale-free coupling in multiple signals simultaneously, enabling real-time applications.

Keywords:
bivariatedetrended cross-correlation analysisdetrended fluctuation analysisfractalfractal connectivitylong-range couplingreal-time

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Area of Science:

  • Complex Systems Analysis
  • Signal Processing
  • Neuroscience

Background:

  • Assessing power-law cross-correlations is crucial in fields like neuroscience and finance.
  • Current methods for detrended cross-correlation analysis (DCCA) are computationally intensive and offline.
  • Real-time analysis is needed for applications like mental state monitoring and financial forecasting.

Purpose of the Study:

  • To develop a novel, efficient, real-time algorithm for DCCA.
  • To generalize the algorithm for analyzing multiple signals and their pairwise relationships.
  • To enable real-time estimation of the detrended cross-correlation coefficient (DCCC).

Main Methods:

  • Introduced a new formula for real-time DCCA scaling function estimation.
  • Utilized matrix notation for simultaneous analysis of multiple signal pairs.
  • Integrated parallel estimation of individual signal detrended fluctuation analysis (DFA) scaling functions.

Main Results:

  • The proposed algorithm achieves precision comparable to offline DCCA methods.
  • Demonstrated substantial improvements in computational efficiency.
  • Successfully applied the method to multi-channel electroencephalographic (EEG) recordings for mental state monitoring.

Conclusions:

  • The developed algorithm provides a precise and efficient solution for real-time cross-correlation analysis.
  • Enables simultaneous, real-time assessment of scale-free coupling among multiple processes.
  • Shows significant potential for real-time applications in neuroscience and financial markets.