| Nonlinear Dynamics, Psychology, and Life Sciences, Vol. 14, Iss. 1, Jan, 2010, pp. 1-13 @2010 Society for Chaos Theory in Psychology & Life Sciences Complexity Loss in Physiological Time Series of Patients in a Vegetative State Abstract: Consciousness has not yet been satisfactorily defined because of its
puzzling nature which involve the perception of the environment (perceptual awareness)
and of the self (self-awareness). Current available methods fail in establishing
prognosis in patients with vegetative state (VS): to our mind, this failure stems
from the heterogeneous localization of brain damages causing VS and from available
approaches tending to investigate self-awareness separately from perceptual
awareness, whereas consciousness should be explored as a single and indivisible
whole. Moving from the assumption that consciousness depends on the normal
activity of wide neural networks, that may be regarded as complex systems whose
outputs show a nonlinear behaviour, we propose a nonlinear approach applied
to electroencephalographic (EEG) signal, aimed at exploring residual neural
networks complexity in patients with VS. For this objective the EEG recording
of 10 patients previously admitted to our department were retrospectively analyzed
and compared with those of ten matched healthy control subjects. Approximate
Entropy (ApEn) was calculated from the average values of time series with fixed
input variables. Mean ApEn values were lower in patients than in controls
(tsub 18=12.3, p < 0.001). ApEn is able to discriminate patients from controls
thus supporting the hypothesis about a decreased neural networks complexity
in VS. Keywords: approximate entropy, vegetative state, nonlinear, complexity, coma |