Application of cross-channel multiscale permutation entropy in measuring multichannel data complexity.

Area of Science:

• Nonlinear dynamics
• Complex systems analysis
• Information theory

Background:

• Entropy is crucial for understanding complex systems, with permutation entropy widely used for its efficiency.
• Existing permutation entropy methods struggle to capture cross-channel information in multi-channel datasets.
• Analyzing interactions between channels is vital in fields dealing with complex, multi-channel data.

Purpose of the Study:

• To develop a novel algorithm for effectively capturing cross-channel information in multi-channel datasets.
• To enhance the capability of permutation entropy methods for analyzing complex systems with multiple data channels.
• To improve the distinction between different data types using multi-channel entropy analysis.

Main Methods:

• Introduction of a cross-channel multiscale permutation entropy algorithm.
• Modification involves concurrent frequency counting of specific events during calculation.
• Improved phase space reconstruction and mapping for multi-channel data.

Main Results:

• The proposed algorithm effectively captures cross-channel information from multi-channel datasets.
• Demonstrated superiority in distinguishing between different types of data through simulations and real-world analysis.
• Enhanced capability of multi-channel permutation entropy methods for information extraction.

Conclusions:

• The cross-channel multiscale permutation entropy algorithm significantly improves the analysis of multi-channel complex systems.
• The method offers a more effective way to uncover information across different channels compared to existing techniques.
• The algorithm's improvements are applicable to various multi-channel permutation entropy variants, broadening its utility.