Multivariate Multiscale Entropy Analysis Algorithm Implementation

The multivariate multiscale entropy analysis program is essential for conducting sophisticated signal investigation. The algorithm implementation, available from a scholar's website, employs a multi-layered approach to quantify signal complexity across different temporal scales. Key implementation features include: - Adaptive coarse-graining methodology for multiple time scales - Multivariate entropy computation using joint probability distributions - Embedded dimension optimization for state-space reconstruction Researchers can leverage this program to perform comprehensive signal analysis at varying resolution levels, extracting detailed information about signal characteristics and underlying patterns. The analytical framework facilitates deeper investigation into signal complexity and nonlinear dynamics through: - Cross-dimensional entropy calculations - Scale-dependent complexity metrics - Statistical validation of multiscale properties By implementing this multivariate multiscale entropy approach, researchers gain enhanced analytical insights and more accurate characterizations of complex signal behaviors. The code structure supports customizable parameters for different signal types and research requirements, including adjustable scale factors and entropy threshold configurations.