Key result
Multivariate multiscale entropy (MMSE) analysis enables the assessment of structural complexity in multivariate physical or physiological systems, supported by synthetic and real-world data.
The introduction of multivariate multiscale entropy allows for the assessment of structural complexity in multichannel physiological and physical systems.
MMSE supports complexity analysis in multichannel physiological signals; leaves open clinical validation in cardiovascular applications.
Multivariate physical and biological recordings are common and their simultaneous analysis is a prerequisite for the understanding of the complexity of underlying signal generating mechanisms. Traditional entropy measures are maximized for random processes and fail to quantify inherent long-range dependencies in real world data, a key feature of complex systems. The recently introduced multiscale entropy (MSE) is a univariate method capable of detecting intrinsic correlations and has been used to measure complexity of single channel physiological signals. To generalize this method for multichannel data, we first introduce multivariate sample entropy (MSampEn) and evaluate it over multiple time scales to perform the multivariate multiscale entropy (MMSE) analysis. This makes it possible to assess structural complexity of multivariate physical or physiological systems, together with more degrees of freedom and enhanced rigor in the analysis. Simulations on both multivariate synthetic data and real world postural sway analysis support the approach.
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Ahmed et al. (2011) studied this question. Multivariate multiscale entropy (MMSE) analysis was evaluated. Multivariate multiscale entropy (MMSE) analysis enables the assessment of structural complexity in multivariate physical or physiological systems, supported by synthetic and real-world data.
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