Advantages and problems of nonlinear methods applied to analyze physiological time signals: human balance control as an example

Abstract

Physiological processes are regulated by nonlinear dynamical systems. Various nonlinear measures have frequently been used for characterizing the complexity of fractal time signals to detect system features that cannot be derived from linear analyses. We analysed human balance dynamics ranging from simple standing to balancing on one foot with closed eyes to study the inherent methodological problems when applying fractal dimension analysis to real-world signals. Higuchi dimension was used as an example. Choice of measurement and analysis parameters has a distinct influence on the computed dimension. Noise increases the fractional dimension which may be misinterpreted as a higher complexity of the signal. Publications without specifying the parameter setting, or without analysing the noise-sensitivity are not comparable to findings of others and therefore of limited scientific value.

Lower complexity of motor primitives ensures robust control of high-speed human locomotion

“…Additionally, 270 the question remains as to why HFD values are lower when the walking or running speed increases and in walking compared to running. Due to its definition (EQ4), the HFD depends on the signal-to-noise ratio [61], but some precautions can be taken to reduce the influence of the signal-to-noise ratio on the outcomes (i.e. minimum subtraction and normalisation to the maximum).…”

Scaling oscillatory platform frequency reveals recurrence of intermittent postural attractor states

“…Previous studies on oscillatory platform motion over various time scales have demonstrated the convergence towards and divergence from stable postural coordination between CoM and platform motion 22 , 24 . Other studies have also illustrated the chaotic dynamics 31 , 32 and fractal properties of postural swaying during upright quiet standing 33 , 34 , and also in human locomotion 35 . However, progressive scaling of platform frequency would provide us a closer insight into the postulated intermittent coupling of CoM-platform oscillators, their nature of emerging attractor states, and the duration of trapped attractor states.…”

Análise Da Sensibilidade Dos Indicadores Clássicos Da Rqa

“…Porém, como mostrado nos trabalhos [6][7][8], estes indicadores se mostram sensíveis a diversos parâmetros relacionados ao processo de aquisição, sendo estes o tamanho da amostra, a frequência de amostragem, o tipo do experimento, a variável analisada, o ruído. Isto ocorre devido à variabilidade do sinal com o decorrer do tempo (em função dos parâmetros supracitados), criando assim um sistema dinâmico variante no tempo, característica que influência diretamente os estimadores dinâmicos.…”

“…Além disso, contém vários graus de liberdade, não linearidades e ruídos, devido aos mais diversos fatores internos e externos [5,6], necessitando, portanto, de indicadores que quantifiquem o sinal de forma robusta a pequenos erros e variações não controláveis.…”

“…Entre os principais indicadores de estabilidade e variabilidade estão a Raiz Quadrática Média (RMS), os expoentes de Lyapunov, a entropia amostral (SampEn), a dimensão de correlação, a dimensão de Higuche e a análise de quantificação de recorrência (RQA) [6][7][8]. Porém, como mostrado nos trabalhos [6][7][8], estes indicadores se mostram sensíveis a diversos parâmetros relacionados ao processo de aquisição, sendo estes o tamanho da amostra, a frequência de amostragem, o tipo do experimento, a variável analisada, o ruído.…”