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Summary

This summary is machine-generated.

This study introduces a new method analyzing local complex structures in recurrence plots (RPs) by combining determinism, laminarity, and recurrence rate (DLR). This DLR approach significantly outperforms traditional recurrence quantification analysis (RQA) in discriminating dynamic systems and physiological data.

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Area of Science:

Nonlinear Dynamics
Time Series Analysis
Biomedical Engineering

Background:

Recurrence plots (RPs) capture nonlinear invariants and trajectory characteristics.
Recurrence Quantification Analysis (RQA) traditionally quantifies diagonal and vertical structures in RPs.
Existing RQA methods may not fully exploit the information within complex RP structures.

Purpose of the Study:

To explore rich information in RPs by quantifying local complex structures.
To develop and evaluate a novel approach combining determinism, laminarity, and recurrence rate (DLR) in a moving metawindow over RPs.
To compare the discriminatory power of the DLR approach against established RQA variables.

Main Methods:

Developed a DLR analysis method using a metawindow over RPs.
Evaluated DLR in discriminating nonlinear dynamic series from the Lorenz system.
Assessed DLR in discriminating human heart rate regulation data (normal vs. congestive heart failure).

Main Results:

The DLR approach demonstrated significantly higher discriminatory power than seven major RQA variables in both experiments.
DLR achieved discriminatory power 7.41 times better than RQA for the Lorenz system and 2.09 times better for heart rate data.
Optimal discriminating RP structures were found to be neither purely diagonal nor vertical.

Conclusions:

Local complex RP structures contain valuable information not fully captured by traditional RQA.
The DLR method offers a more effective approach for dynamic discrimination using RPs.
Future research should focus on analyzing complex RP structures to enhance RP analysis effectiveness.