Analysis of postural sway using entropy measures of signal complexity

A stochastic complexity analysis is applied to centre-of-pressure (COP) tim e series, by using different complexity features, namely the spectral entro py, the approximate entropy, and the singular value decomposition spectrum entropy. A principal component analysis allows an estimate of the overall s ignal complexity in terms of the ensemble complexity score; the difference in values between open-eyes (OE) and closed-eyes (CE) trials is used for cl ustering purposes. In experiments on healthy young adults, the complexity o f the mediolateral component is shown not to depend on the manipulation of vision. Conversely, the increase of the anteroposterior complexity in OE co nditions can be statistically significant, leading to a functional division of the subjects into two groups: the Romberg ratios (RRs), namely the rati os of the CE measure to the OE measure, are: RR=1.19+/-0.15 (group 1 subjec ts), and RR=1.05+/-0.14 (group 2 subjects). Multivariate statistical techni ques are applied to the complexity features and the parameters of a postura l sway model recently proposed; the results suggest that the complexity cha nge is the sign of information-generating behaviours of postural fluctuatio ns, in the presence of a control strategy which aims at loosening long-rang e correlation and decreasing stochastic activity when visual feedback is al lowed.