Multivariate entropy analysis with data-driven scales

M. U. Ahmed, N. Rehman, D. Looney, T. M. Rutkowski, P. Kidmose, D. P. Mandic
2012 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)  
A data-adaptive algorithm for the entropy-based analysis of structural regularities (complexity) in multivariate signals is proposed. This is achieved by combining multivariate sample entropy with a multivariate extension of empirical mode decomposition, both datadriven multiscale techniques. The proposed analysis across dataadaptive scales makes the approach robust to nonstationarity, a critical issue with information theoretic measures. Simulations on synthetic and real-world physiological
more » ... a support the approach and validate the hypothesis of increased complexity for unconstrained as compared to constrained (due to e.g. ageing or illness) biological systems. Index Terms-Multivariate sample entropy, multivariate empirical mode decomposition, multivariate multiscale entropy, dynamical complexity, complexity of physiological data
doi:10.1109/icassp.2012.6288770 dblp:conf/icassp/AhmedRLRKM12 fatcat:b4e7higup5h2rnmt76zvlkespu