Fractal time series analysis of postural stability

The conventional approach for evaluating postural balance with a stabilometer involves analysis of parameters such as mean velocity, length of sway path and sway area. Evaluation of these parameters is very important in assessment of the degree of the balance disorder, but they have not been applied to diagnose lesions of balance disorders. Because the sensorimotor control of maintaining balance during standing involves complex mechanisms of integrating multiple sets of information, it is difficult to clarify the characteristics of balance disorders derived from the input of each organ. Another approach for evaluating postural balance is based on nonlinear time series methods including fractal analysis. We used detrended fluctuation analysis (DFA) on stabilometric time series data. The DFA scaling exponent quantifies fractal-like autocorrelation properties and the complexity of the signals. The aim of this study was to investigate postural control strategy in the elderly and patients with balance disorders. Thirty-eight healthy subjects, 50 patients with unilateral vestibular dysfunction (UVD), 15 patients with cerebellar ataxia (CA) participated in this study. DFA scaling exponents can be divided into the short-range (α short) and long-range (α long) scaling components. The value of α long in the healthy subjects was nearly close to 1 (time series data of α=1 corresponds to 1/f noise). Compared to 13 young adults (23-38 years), the value of α long did not show significant differences in 19 young-elderly but was reduced in 14 late-elderly. Compared to the healthy subjects, the value of α long did not show a significant difference in patients with UVD and reduced significantly in patients with CA. The value of α long of patients with UVD in the acute stage was less than 1, but that of the patients with UVD in the subacute stage significantly increased and showed nearly 1. The value of α long of DFA may be a functional indicator of the attitude control system.