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

Entropy is a pivotal concept in nonlinear dynamics, revealing chaos, self-organization, and information transmission in complex systems. Permutation entropy, due to its computational efficiency and lower data length requirements, has found widespread use in various fields. However, in the age of multi-channel data, existing permutation entropy methods are limited in capturing cross-channel information. This paper presents cross-channel multiscale permutation entropy algorithm, and the proposed algorithm can effectively capture the cross-channel information of multi-channel dataset. The major modification lies in the concurrent frequency counting of specific events during the calculation steps. The algorithm improves phase space reconstruction and mapping, enhancing the capability of multi-channel permutation entropy methods to extract cross-channel information. Simulation and real-world multi-channel data analysis demonstrate the superiority of the proposed algorithm in distinguishing different types of data. The improvement is not limited to one specific algorithm and can be applied to various multi-channel permutation entropy variants, making them more effective in uncovering information across different channels.